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Integrated exemplar-based template matching and statistical modeling for continuous speech recognition
EURASIP Journal on Audio, Speech, and Music Processing volume 2014, Article number: 4 (2014)
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We propose a novel approach of integrating exemplar-based template matching with statistical modeling to improve continuous speech recognition. We choose the template unit to be context-dependent phone segments (triphone context) and use multiple Gaussian mixture model (GMM) indices to represent each frame of speech templates. We investigate two different local distances, log likelihood ratio (LLR) and Kullback-Leibler (KL) divergence, for dynamic time warping (DTW)-based template matching. In order to reduce computation and storage complexities, we also propose two methods for template selection: minimum distance template selection (MDTS) and maximum likelihood template selection (MLTS). We further propose to fine tune the MLTS template representatives by using a GMM merging algorithm so that the GMMs can better represent the frames of the selected template representatives. Experimental results on the TIMIT phone recognition task and a large vocabulary continuous speech recognition (LVCSR) task of telehealth captioning demonstrated that the proposed approach of integrating template matching with statistical modeling significantly improved recognition accuracy over the hidden Markov modeling (HMM) baselines for both TIMIT and telehealth tasks. The template selection methods also provided significant accuracy gains over the HMM baseline while largely reducing the computation and storage complexities. When all templates or MDTS were used, using the LLR local distance gave better performance than the KL local distance. For MLTS and template compression, KL local distance gave better performance than the LLR local distance, and template compression further improved the recognition accuracy on top of MLTS while having less computational cost.
1 Introduction
In speech recognition, hidden Markov modeling (HMM) has been the dominant approach since it provides a principled way of jointly modeling speech spectral variations and time dynamics. However, HMM has the shortcoming of assuming the observations being independent within each state, which makes it ineffective in modeling the fine details of speech temporal evolutions that are important in characterizing nonstationary speech sounds [1]. Time derivatives of cepstral coefficients [2] are widely used to supplement time dynamic information to speech feature distributions. Trajectory model [3] introduces time-varying covariance modeling to capture temporal evolutions of speech features. Additionally, approaches like segment models [4, 5] and long-contextual-span model of resonance dynamics [6] have been proposed for similar purposes.
Exemplar-based methods have the potential in addressing the deficiency of HMMs and in recent years they have drawn renewed attention in the speech recognition community [7, 8], such as sparse representations (SRs) [9] and template matching [10, 11]. Template-based methods make direct comparisons between a test pattern and the templates of training data via dynamic time warping (DTW), and potentially they can capture the speech dynamics better than HMMs. Template-based methods were originally used to recognize isolated words or connected digits with good performances [12]. Until recently, template-based methods had been impractical for large tasks of speech recognition, since feature vectors of training templates need to be stored in computer memory. With today's rapid advance in computing power and memory capacity, template-based methods are investigated for large recognition tasks and promising results are reported [10, 11, 13–18]. However, they are still difficult to use in large vocabulary continuous speech recognition (LVCSR) due to their needs for intensive computing time and storage space. The newly proposed methods, such as template pruning and filtering [19], template-like dimension reduction of speech observations [20], and template matching in the second-pass decoding search [21], are beginning to address this problem. In general, there is a tradeoff between the costs in computation and space and the accuracy in recognition.
Considering the pros and cons of HMMs and template methods, i.e., HMM-based statistical models are effective in compactly representing speech spectral distributions of discrete states but are ineffective in representing the fine details of speech dynamics, while template matching captures well the speech temporal evolutions but demands much larger computational complexity and memory space, it appears plausible to integrate the two approaches so as to exploit their strengths and avoid their weaknesses. In the current work, we propose a novel approach of integrating exemplar-based template matching with statistical modeling. We construct triphone context-dependent phone templates to preserve the time dynamic information of phone units and use phonetic decision trees to generate templates of tied triphone units, which improves the reliability of triphone templates and covers unseen triphones by some triphone clusters. The load on memory storage is reduced by using Gaussian mixture model (GMM) indices to represent the speech frames of the templates. It is worth noting that Gaussian indices were previously used to represent speech frames in speech segmentation [22], speech separation [23], and keyword spotting [24–26]. To facilitate comparison of the templates labeled by GMM indices, we propose the local distances of log likelihood ratio (LLR) and Kullback-Leibler (KL) divergence for DTW-based template matching. To further reduce the costs of memory space and computation, we propose template selection methods to generate template representatives based on the criteria of minimum distance (MDTS) and maximum likelihood (MLTS) and we also propose a template compression method to integrate information from training templates to obtain more informative template representatives. In the recognition stage, the GMMs and the templates are used together by DTW with the proposed local distances. The proposed methods have been applied to lattice rescoring on the tasks of TIMIT [27] phone recognition and telehealth [28] large vocabulary continuous speech recognition, and they have led to consistent error reductions over the HMM baselines.
This paper is organized as follows. In Section 2, we discuss the related work for template-based speech recognition and provide an overview of our proposed system. In Section 3, we describe the proposed methods for template construction, matching, and clustering. In Section 4, we discuss the proposed methods for template representative selection and compression. In Section 5, we present evaluation results on the task of TIMIT phone recognition and the task of telehealth LVCSR. Finally in Section 6, we give our conclusion and discuss future work.
2 Related work and system overview
2.1 Related work
Continuous speech recognition using template-based approaches has gained significant attention over the past several years. In [10], a top-down search algorithm was combined with a data-driven selection of candidates for DTW alignment to reduce search space, together with a flexible subword unit selection mechanism and a class-sensitive distance measure. On the Resource Management task, although the performance of the template matching system fell below the best published HMM results, the word error patterns of the two types of systems were found to be different and their combination was beneficial. In [13], an episodic-HMM hybrid system was proposed to exploit the ability of HMMs in producing high-quality phone graphs as well as the capability of an episodic memory in accessing fine-grained acoustic data for rescoring, where template matching was performed by DTW using the Euclidean distance. This system was evaluated on the 5k-word Wall Street Journal (WSJ) task and it showed a comparable performance with state-of-the-art HMM systems. In [18], prosodic information of duration, speaking rate, loudness, pitch, and voice quality was integrated with template matching through conditional random fields to improve recognition accuracy. On the Nov92 20k-word trigram WSJ task, the proposed method improved the state-of-the-art template baseline without prosodic information and led to a relative word error rate reduction of 7%. To make the template-based approach realistic for hundreds of hours of speech training data, a data pruning method was described for template-based automatic speech recognition in [19]. The pruning strategy worked iteratively to eliminate more and more templates from an initial database, and at each iteration, the feedback for data pruning was provided by the word error rate of the current model. This data pruning reduced the database size or the model size by about 30%, and consequently saved the computation time and memory usage in speech recognition. In [21], exemplar-based word-level features were investigated for large-scale speech recognition. These features were combined with the acoustic and language scores of the first-pass model through a segmental conditional random field to rescore word lattices. Since the word lattices helped restrict the search space, the templates were not required to cover the full training data, and the templates were also filtered to a smaller set to reduce computation cost and improve robustness. Experimental results showed that the template-based approach obtained a slightly better performance than the baseline system in Voice Search and YouTube tasks.
Relative to the above-discussed efforts, our approach as proposed in the current work falls into the hybrid category, but our integration of statistical modeling and template representation and matching are tighter, since we not only rescore the lattices generated by the HMM baseline, but we also use the baseline phonetic decision tree (PDT) structures to define the tied triphone templates, representing the template frames by the GMMs and using the LLR and KL distances to measure the differences of speech frames represented in this way. In the aspect of reducing computation and memory costs, we absorb the training data information into template representatives through clustering and estimation, rather than selecting a subset of training data as the templates. On the TIMIT and telehealth tasks, we are able to show statistically significant improvements in phone and word accuracies, respectively, over the HMM baselines.
2.2 System overview
The overall architecture of the proposed template matching method is described in Figure 1. In the training stage, Viterbi alignment is performed on the training data by the baseline model to determine the phone template boundaries; using the PDT-based triphone state tying structures of the baseline system, template clustering is performed to generate tied triphone templates (Section 3.3); using the GMM codebook derived from the baseline model, the template frames are labeled by the GMMs (Section 3.1); template selection and compression are further performed to generate the template representatives (Section 4). In the test stage, the baseline model is first used to perform decoding search on a test speech utterance to generate a word lattice; the test speech frames are labeled by the GMMs in the same way as in training; template matching and best path search are then performed on the word lattice to generate the rescored sentence hypothesis (Section 3.3).
3 Template representation, matching, and clustering
3.1 Template representation
We choose the template unit to be context-dependent phone segments, the context being the immediately left and right phones of each phone segment, and we refer the context-dependent templates as triphone templates. We first carry out forced alignments of training speech data with their transcriptions to obtain phone boundaries which define the phone templates. We then use a GMM codebook {m1, m2, …, m N } that consists of the GMMs of the phonetic-decision-tree tied triphone states in the baseline HMMs to label the template frames, where N is the total number of GMMs from the HMM baseline. To do so, we compute the likelihood scores of a feature vector or frame (these two terms are used interchangeably with the understanding that a feature vector is normally extracted from a frame of data), x t ∈ Rd (d is the dimension of a real-valued feature vector), of a phone template by all GMMs and take the n GMMs that give the top n likelihood scores, , to label x t . Each GMM index is also associated with a weight that is defined to be proportional to the likelihood score , with . A template frame is therefore represented as
In general, n < < d, and hence storing the template frames in GMM indices requires a much smaller space than storing the feature frames for the Templates.
3.2 Template matching
In using DTW to measure the dissimilarity between two speech utterances, the allowed range of speaking rate variations can be specified by local path constraints [12]. Let d(i, j) denote the local distance between the i th and the j th frames of two sequences under comparison and D(i, j) denote the cumulative distance between the two sequences up to the time i and j. The symmetric constraint that we adopt here is defined as
Given a sequence S x representing a template and a sequence S y representing a test segment, their average frame distance is calculated as
where are the warping functions that map S x and S y to a common time axis, and N is the warping path length. Considering the fact that in HMM-based decoding search the acoustic score of a test segment is the sum of its frame log likelihood scores (the segment acoustic score is therefore the average frame score scaled by the length of the segment), we define the distance between the template S x and the test segment S y in the similar way as
where L is the length of the test segment S y . Through scaling the average frame distance by the test segment length, the acoustic scores for different hypotheses of a test speech utterance (which in general consists of many segments) can be directly compared in template matching, as in HMM-based decoding search. Note that without the normalization by N in Equation 3, a template matching score for a speech segment would be affected by the length of the time warping path, which may vary with different templates; on the other hand, if the rescaling by L is not adopted, then the total distance on a decoding path would be dependent on the number of test segments in the path but not the lengths of these segments.
Commonly used local distances, such as Euclidean or Mahalanobis distances, compute the difference between two feature vectors directly [10], and they are thus of a feature-feature type. Let x and y represent two frames under comparison. The Euclidean distance is
and the Mahalanobis distance is
with Σ as the covariance matrix estimated from training data.
When the template frames are represented by GMM indices, the Euclidean and Mahalanobis distances are no longer suitable. One possibility is to use negated log likelihood (NLL) score as a local distance. Let be the frames of a test segment and a training template, respectively, and assume that is labeled by a GMM . The NLL distance is then
When is represented by n GMMs with the weights , the NLL distance becomes
The NLL distance is of the feature-model type, as it does not use the information of the GMM labels on the test segment frames. The proposed log likelihood ratio and KL divergence distances make use of the GMM labels on both the test and the training frames. These two model-model distances are described below.
3.2.1 Log likelihood ratio local distance
Assuming that the test frame x t is labeled by a GMM and the training frame is labeled by a GMM . The LLR local distance between x t and is then defined as follows:
The LLR distance contrasts the fit score of a test frame with its best model against its fit score with the best model of the template frame, and it therefore compares the two frames indirectly through the models. The LLR distance is nonnegative when 1-best GMM is used in frame labeling. When using multiple GMM indices for speech frame representation, the nonnegativity also holds if the weights are kept uniform, but it is not guaranteed if the weights are nonuniform, where the latter is due to the fact that although the GMM scores of the numerator are not smaller than those of the denominator, a skew in the denominator's weights toward some large GMM scores may make the denominator larger than the numerator. On the other hand, since what we really need is the difference of the numerator and denominator log likelihood scores as the dissimilarity between a test frame and a template frame, while a strict-sense log likelihood ratio is not needed here as in statistic hypothesis testing, we can therefore simply take the absolute value of the log likelihood score difference as the distance measurement which is also the rectified LLR given in Equation 10:
(it is worth mentioning here that although getting a negative log likelihood ratio is a mathematical possibility, it never occurred in the experiments described in Section 5).
3.2.2 KL divergence local distance
In either the NLL distance or the LLR distance, the feature vector x t is involved in the distance calculation. Here we consider measuring the local distance between two frames without using the feature vectors. KL divergence is widely used for measuring the difference between two probability distributions [29]. Since the frames are represented by GMM indices, the KL divergence between GMMs becomes a natural choice for indirectly measuring the dissimilarity of two frames. Because there is no closed-form expression for KL distance of GMMs, we use the Monte Carlo sampling method of Hershey and Olsen [30] to compute the divergence from a GMM m x to a GMM m y as
where the x i s are i.i.d. samples generated from the GMM m x . Since the KL divergence is asymmetric, we further define a symmetric KL distance as
The local distance between the two frame vectors is then calculated as
3.3 PDT-based template clustering and matching score calculation
Considering the fact that certain triphone contexts may rarely occur or even be missing in a training set, we investigate tying triphone templates into clusters of equivalent contexts to improve the reliability of template matching as well as to handle unseen triphones in recognition. Among many possible clustering algorithms, we decide to utilize the PDT tying structures of the triphone states in the baseline HMMs directly to cluster triphone segments, since the tying structure of a phone state indicates partial similarities among triphone segments. We assume that each phone HMM has three emitting states as commonly used in HTK [31]. For the triphone templates of each monophone, we keep the three tying structures defined by the three emitting states of the corresponding phone HMM and use them jointly in template matching.
Specifically, in matching a test speech segment against a triphone arc in a word lattice, we first identify the three tied triphone clusters by answering the phonetic questions in the PDTs, and for an identified cluster i with k i templates, we then choose best-matching templates and average their matching scores for the test segment, and we further average the three scores of the three clusters as the matching score between the speech segment and the triphone arc. Using the square-root rule helps compress the variations of the number of templates k i used in computing the scores, since the number often vary largely in different triphone clusters. The rule is also analogous to the K-nearest neighbor (KNN) method where K is set as the square root of the training sample size [32].
Figure 2 illustrates the process of computing template matching score for lattice rescoring. It shows a phone lattice and a test speech segment X extracted from a speech utterance according to the start and end time of the phone arc P that has a predecessor phone P L and successor phone P R . Figure 3 illustrates the way that the matching scores of X with the three triphone template clusters containing P L − P + P R are averaged to one matching score, which is used to replace the original acoustic score in the phone lattice for the phone arc P.
4 Template selection and compression
When the above-described template matching is used for lattice rescoring in LVCSR, the computation and storage overheads are still high. However, certain redundancies in the training templates can be reduced to improve computation and storage efficiency. We propose three methods of template selection and compression to address this problem. In template selection, the goal is to choose a small subset of templates as the representatives for the full set of training templates. In template compression, new GMMs are generated for labeling the frames of the selected template representatives so as to better capture the information in the training Templates.
4.1 Minimum-distance-based template selection
Agglomerative clustering [33] is a hierarchical clustering algorithm and it is widely used in pattern recognition, including speech recognition [34]. For selecting template representatives, we use the agglomerative clustering algorithm to further cluster the templates in each tied triphone cluster at a PDT leaf node, which recursively merges two closest clusters into one cluster until only one cluster is left. Given a distance function D(C i , C j ) for two clusters, the following procedure describes the algorithm for clustering m templates {s1, s2,…,s m } in a leaf node of a PDT:
Initialize the template set Z 1 = {{s 1}, {s 2}, …, {s m }} with each template s i being a cluster.
For n = 2,…,m: Obtain the new set Z n by merging the two clusters C i and C j in the set Z n − 1 with the distance D(C i , C j ) to be the minimum among all existing distinct cluster pairs. Stop the clustering process if the number of clusters in the set Z n drops below a threshold.
The cluster distance function D(C i , C j ) is commonly defined by the distance of their elements D(s x , s y ), and the average distance measure is adopted here [33]:
Note that D(s x , s y ) is the DTW distance of two templates as defined in Section 3.2, and in this step, the local distance d is the Euclidean distance of two frames.
To select a template representative for a cluster, we use the minimum distance from a template to all other templates in the cluster as the criterion, and therefore the method is called minimum distance template selection (MDTS). Given a cluster C i , the template-to-cluster distance is defined as follows [33]:
and the template s* is selected as the representative for the cluster C i if its distance to the rest of the templates in the cluster is the minimum, i.e.,
The frames of the selected template representatives are subsequently indexed by their n-best GMMs according to Section 3.1.
4.2 Maximum-likelihood-based template selection
In maximum likelihood template selection, each frame of a cluster center s* as generated by the MDTS method is relabeled by a set of GMMs that are selected by using a maximum likelihood criterion, so as to make the representative better characterize the templates in each cluster. For maximum likelihood template selection (MLTS), we use the DTW described in Section 3.2 to align the templates in a cluster C i to the MDTS-initialized template center s*. Figure 4 illustrates an outcome of aligning the sequences s1,…, s N to s* in C i , where the frames of the sequences s1,…, s N , respectively, are aligned to the frame of the cluster center s*. The following procedure describes the MLTS method that is applied to relabel of s* by using the aligned frames :
where is the j th GMM in M i .
Pool the distinct GMMs which are used to label the frames in X into a local GMM set M.
Use the K-medoids algorithm [33] with the KL distance to partition the GMM set M into l clusters M i , i = 1,…, l, where each M i defines a subset of frames that are labeled by the GMMs in M i .
For i = 1,…, l: Use the maximum likelihood criterion to select a GMM of M i as the cluster center for M i :
For i = 1,…, l: Calculate the weight w i for each GMM cluster center , which is proportional to the likelihood of X evaluated by , i.e., :(18)
After the relabeling, the frame x t is represented by and w i , i = 1,…, l. The MLTS procedure is applied to each frame of s*. The resulting representation of s* has a form similar to what is described in Section 3.1, with the difference that the best-fitting n GMMs of the baseline HMMs are used to label a frame in Section 3.1, but here the template frames that are aligned to a frame of the MDTS representative are used to select a set of l GMMs to relabel the frame of the representative.
4.3 Template compression
The template compression method aims at taking in more information from the original templates for the template representatives. For each frame of a template representative, instead of selecting only one GMM and excluding the rest of the GMMs for a cluster M i as in MLTS, here we merge the original GMMs in each cluster M i into a new GMM and use the l new GMMs from the l clusters M i , i = 1,…, l to relabel the frame. To reduce the negative effect of outlier templates, for each GMM in a cluster M i , we calculate its distance to the cluster center based on the KL distance . From the distances of M i , the mean and the standard deviation σ are computed. If a GMM is t times standard deviation away from , i.e.,
then it is considered an outlier and is removed from the merging process. Suppose that after removing the outliers, there are n G GMMs left in M i . We first pool the component Gaussian densities from the n G GMMs and normalize the weight of each Gaussian component by n G . We then merge the pooled Gaussian components according to the criterion of minimum entropy increment. The entropy increase due to merging two Gaussian components f i ~ N(µ i , Σ i ) and f j ~ N(µ j , Σ j ) into N(µ, Σ) is defined as [35]:
where w i and w j are the normalized mixture weights for f i and f j . The mean μ, covariance Σ, and mixture weight w of the newly generated Gaussian component are defined as
The Gaussian components are merged iteratively until the number of components in M i is below a preset threshold. The remaining Gaussian components are used to construct a new GMM, and the new GMM is used as one of the l GMMs to label the corresponding frame of the template representative.
The flowcharts of the above-discussed three template selection and compression methods are given in Figure 5. As shown in the figure, the three methods share the same template representatives that are selected from the original GMM-labeled templates. While MDTS stops here, MLTS reselects the GMM labels for the representative frames, and template compression generates new GMMs and uses them to relabel the frames of the template representatives. As are shown in the experimental results of Section 5, the refinements on the GMM labels make the template representatives more effective, and when coupled with a proper local distance they allow using only a small fraction of template representatives in lattice rescoring with little performance loss.
5 Experimental results
We performed speaker-independent phone recognition on the task of TIMIT [27] and speaker-dependent large vocabulary speech recognition on the task of telehealth captioning [28]. The experimental outcomes were measured in phone accuracy and word accuracy, respectively, for TIMIT and telehealth through aligning each phone or word string hypothesis against its reference string by using the Levenshtein distance [31].
5.1 Corpora
The TIMIT training set consisted of 3,696 sentences from 462 speakers and the standard test set included 1,344 sentences spoken by 168 speakers. The telehealth task included spontaneous speech from five doctors and the vocabulary size was 46,000. A summary of the Telehealth corpus is given in Table 1, where the word counts from the transcription texts are also listed. For a detailed description of this task, please refer to [28].
5.2 Experimental set up and lattice rescoring
For both tasks of TIMIT and telehealth, the speech features consisted of 13 MFCCs and their first- and second-order time derivatives, and crossword triphone acoustic models were trained by using HTK toolkit. In calculating a KL distance between two GMMs [30], 10,000 Monte Carlo simulation data samples were generated.
For the TIMIT dataset, the set of 39 phones was defined as in [36], and a phone bi-gram language model (LM) was used (trained from the TIMIT training speech transcripts). The HMM baseline was trained with the GMM mixture sizes of 24; and 1,189 GMMs were extracted for template construction. The total original triphone templates were 152,715 in the training set. Phone lattices were generated for each test sentence by HTK. The average number of nodes per lattice was in the order of 850, and the average number of arcs was in the order of 2,350.
For the telehealth task, speaker-dependent acoustic models were trained for five healthcare provider speakers Dr. 1 to Dr. 5. In the baseline acoustic model, each GMM included 16 Gaussian components and on average, 1,905 GMMs were extracted from the baseline HMMs of each of the five doctors. The average number of triphone templates was 181,601 per speaker for the five doctors. Trigram language models were trained on both in-domain and out-of-domain datasets, where word-class mixture trigram language models with weights obtained from a procedure of forward weight adjustment were used [37]. For each test sentence, word lattices including phone boundaries were generated by HTK. The average number of nodes per lattice was in the order of 700, and the average number of arcs was in the order of 1,950.
In rescoring a lattice, the acoustic score of each phone arc in the lattice was replaced by its corresponding triphone template matching score, where the distance score of Equation 4 was negated to become a similarity score. By using the acoustic similarity scores and the original language model scores, the best path with the largest sum of acoustic and language model log scores was searched on the lattice using dynamic programming to produce the rescored sentence hypothesis.
5.3 TIMIT phone recognition task
On the TIMIT task, we provide a detailed account of the factors in the proposed template matching methods that affect the rescoring performance, including local distances, number of GMMs employed for frame labeling, template selection, compression methods and their interactions with the local distances, and the percentage of selected template representatives. We also examine the patterns of phone error reduction and look at the cost-performance tradeoffs.
5.3.1 Local distances
In Figure 6, we compare the phone recognition performances by using the HMM baseline and the template-matching-based lattice rescoring with the local distances of Mahalanobis, NLL, LLR, and KL divergence. Except for the baseline and the Mahalanobis distance, each frame of a template or a test speech segment was labeled by 1GMM index. The HMM baseline had the phone accuracy of 72.72%. In template matching, the Mahalanobis and NLL local distances improved the baseline by merely 0.11% and 0.12% absolute, respectively, but the LLR and KL distances improved the HMM baseline by 1.30% and 0.96% absolute, respectively. The LLR distance gave higher phone accuracies than the KL distance did. This may be attributed to the fact that the KL divergence measures the difference between GMM distributions but not directly the difference between feature vectors, whereas the LLR distance contrasts the likelihood scores of two sets of GMMs for each test frame, and therefore it reflects the characteristics of the test frame more closely. Giving the superiority of the proposed LLR and KL distances, we only use these two local distances in the subsequent experiments.
5.3.2 Number of GMMs for frame labeling
In Figure 7, we show the effects of using different numbers of GMMs (n = 1, n = 3, n = 5, and n = 7) in labeling each frame of the templates. For both LLR and KL distances, the accuracy performance peaked when five GMMs were used for frame labeling, and phone accuracies of 74.51% and 74.26% were achieved for LLR and KL distances with absolute improvements of 1.79% and 1.54%, respectively, over the HMM baseline of 72.72%. The results confirmed the advantage of using multiple GMMs for frame labeling over using single GMM, as the former induced smaller quantization errors than the latter. However, using too many GMMs to represent a frame could increase confusion and reduce efficiency. We conducted significance tests on the performance difference between the '5GMMs' case and the HMM baseline. Let x i and y i be the phone recognition accuracy of the i th test sentence for the baseline and a proposed method, respectively. Let t i = y i − x i and denote the sample mean and sample variance of t i as and s2 with the sample size m. The Student's t test statistic is . In the TIMIT standard test set, m = 1,344 and tm − 1,1 − 0.05 = 1.65 for one-sided test. For the LLR and KL local distances, we obtained T > tm − 1,1 − 0.05, and therefore our proposed template matching methods using the LLR and KL distances improved TIMIT phone recognition accuracy significantly over the HMM baseline at the significance level of 0.05. We also used twofold cross-validation on the test set to automatically select the number of GMMs for frame labeling, and the case of 5GMM was selected in each validation set. Therefore, the result of the 5GMM case in Figure 7 also represents an open test performance. In the subsequent experiments, five GMMs were used for labeling each frame.
5.3.3 Template selection and compression
The performances of template selection and compression exhibited a dependency on the local distance measures. Here we discuss how the three methods of (1) MDTS, (2) MLTS, and (3) template compression performed when using the LLR and KL distances and show the results in Figure 8, where the number of template representatives were kept to be 20% of the total templates for the three cases (further details are discussed in Section 5.3.5). In template compression, the threshold t in Equation 19 was set to 2 for removing GMM outliers, and the number of Gaussian components in each merged GMM was 24, the same as the GMM mixture size in the baseline HMMs, with a total of 749 newly generated GMMs for the template representatives. In MDTS, the phone accuracies were 73.82% and 72.70% for the LLR and KL distance, respectively, and in MLTS, the phone accuracies were 74.05% and 73.07% for the LLR and the KL distance, respectively. Relative to MLTS, template compression increased absolute phone accuracy by 0.27% with the KL distance and it decreased absolute phone accuracy by 0.40% with the LLR distances. Several points worth noting in Figure 8 are discussed below.
First, MDTS worked well with the LLR distance but poorly with the KL distance, and vice versa for MLTS and template compression. In MDTS, the template representative frames were labeled in the same way as the test frames, i.e., by the best-fit GMMs of the baseline model, and in this case, a better outcome of LLR than KL is consistent with what was shown in Figure 6 for using all templates. In MLTS, however, the selected template representative frames were relabeled by GMMs to maximize the likelihood of the aligned template frames, and template compression went further by generating new GMMs from the baseline GMMs and used the new GMMs to relabel the representative frames. Because in MLTS or template compression the template representative frames were no longer labeled by the best-fit GMMs, the LLR distance that contrasted the model-frame fit became ineffective in comparison with the KL distance that measures the distance between GMMs.
Second, relative to using all of the original templates as discussed in Section 5.3.2, using 20% template representatives that were selected by MLTS with the KL distance slightly decreased phone accuracy by 0.21% (from 74.26% to 74.05%), but using the template representatives selected by MDTS with the LLR distance significantly decreased phone accuracy by 0.69% (from 74.51% to 73.82%). This difference may be explained by the fact that MDTS simply selects a cluster center as a template representative, but MLTS further refines the GMM indices of each template representative frame to maximize the likelihood of the aligned frames in the corresponding cluster. In this way, MLTS absorbs more information from the training data into the template representatives than MDTS, and so fewer template representatives are needed in MLTS than in MDTS.
Third, with the KL distance, template compression further improved the performance over MLTS, where by using 20% template representatives, phone accuracy was actually improved by 0.06% over the case of using all templates (from 74.26% to 74.32%). This indicates that the new GMMs were more effective in labeling the template representative frames, and the exclusion of the outlier GMMs was helpful, too.
In summary, MDTS worked well with LLR distance, and MLTS and template compression worked well with KL distance. Using the respectively compatible local distances and fixing the selection percentage at 20%, template compression performed the best, MLTS the next, and MDTS the last. Specifically, the accuracy gains over the HMM baseline were 1.6% absolute by template compression with KL, 1.33% by MLTS with KL, and 1.1% by MDTS with LLR. We also conducted the Student's t test on the performance differences between each of the three methods (with respectively compatible distance) and the HMM baseline, and the three methods all significantly improved phone accuracy over the baseline at the level of α = 0.05.
5.3.4 Evaluation on the outlier threshold t
In Table 2, we show how the threshold value t of Equation 19 for removing the GMM outliers affected the recognition performance, where the template selection method was MLTS with the KL distance, and 20% template representatives were selected. Among the four t values studied here, it is observed that t = 2 gave the best phone accuracy performance. Also note that when t = ∞, all GMMs in a cluster were used to generate compressed templates, where the existence of outliers degraded the accuracy performance significantly. Accordingly, the threshold t = 2 was used in all the template compression experiments.
5.3.5 Evaluation on the number of template representatives in template selection methods
In Figure 9, we show how the percentages of template representatives selected from the total templates affect phone accuracies for MDTS and MLTS with their respectively compatible distances. The number of GMM clusters l in MLTS was set to 5, corresponding to using five GMMs to label each frame of a template representative. It is seen from the two curves that with the percentage varied from 100% down to 1%, the phone accuracies decreased for both methods. When 100% templates were used, i.e., without template selection, LLR distance performed better than KL distance, as discussed in Section 5.3.1 and Section 5.3.2. When less than 80% templates were used, MLTS performed better than MDTS since the MLTS templates generalized better than MDTS templates, as discussed in Section 5.3.3. For MDTS, when the selection percentage reduced from 100% to 60%, the phone accuracy dropped rapidly by 0.55% (from 74.51% to 73.96%), and when the selection percentage reduced from 60% to 20%, the phone accuracy reduced slowly by 0.14% (from 73.96% to 73.82%). In contrast, for MLTS, with the selection percentage reduced from 100% to 20%, the phone accuracy went down gradually by 0.21% (from 74.26% to 74.05%). Moreover, both curves went down rapidly when the selection percentage was further reduced below 20%. From Figure 9, we conclude that MLTS is more robust to using a small percentage of template representatives, and the selection percentage of 20% is a reasonable compromise between accuracy performance and computation and storage cost.
5.3.6 Phone accuracy analysis
In order to better understand the effect of the proposed template matching methods, we compare the patterns of TIMIT phone accuracies from using the methods of all templates with the KL and LLR local distances against that of the HMM baseline. Table 3 provides the phone accuracies of the five broad phone classes (vowels, semivowels, stops, fricatives, and nasals) and the accuracy of silence for the HMM baseline and template matching. In Figure 10, we plot the absolute phone accuracy changes of template matching against the HMM baseline. For the vowel class, the KL- and LLR-based template matching produced absolute phone accuracy gains of 4.82% and 4.84%, respectively, and for the semivowel class, the absolute accuracy gains were 4.05% and 4.38%, in the same order. For the stop class, template matching using the LLR distance made an absolute gain of 2.0% while using the KL distance did not help. For the fricative class, phone accuracies were decreased 2.46% and 2.88% by the KL- and LLR-based template matching, respectively. For the nasal class, there were small phone accuracy gains, and for silence, there were small accuracy degradation by template matching, but both changes were small and insignificant.
It is not surprising that the template-based methods produced the largest positive impact on semivowels (largest relative phone error reduction). Semivowels are transient sounds and templates can capture their trajectory information better than HMM. Similarly, some vowel sounds are nonstationary, such as diphthongs or vowels in strong coarticulation. Stops, having the closure and burst pattern, are nonstationary as well and often have short durations, and they are difficult to model by HMM but can be better represented by templates, as reflected in the accuracy gain by the LLR-based template matching. Fricatives are noise like and without clear trajectory patterns, and their boundaries are also difficult to determine, making template-based methods not as effective as HMMs.
5.3.7 Computation time and memory overhead
We first compare the storage space costs of the conventional and the proposed template representation methods, assuming a speech feature vector is 39 dimensional as in the baseline HMM. In conventional template methods that use Mahalanobis local distance, a speech frame is represented by a 39-dimensional vector (float), while in the proposed method a frame is labeled by n GMM indices (short integer) and n − 1 weights (float). On a 32-bit machine and with n = 5 in our experiments, the proposed method used 26 (5 × 2 + (5–1) × 4) bytes per frame versus the conventional method of 156 bytes per frame, which amounts to an 83% saving in storage space. For the TIMIT dataset, there were 152,715 phone templates and the average length of a phone template was eight frames (with the frame shift of 10 ms), giving a total of 1,221,720 frames and an overhead for template storage of 30.3 MB. In template selection, the memory overhead was around 6.1 MB when 20% templates were selected to be the representatives. In template compression, the memory overhead for template storage was the same as in template selection. However, since there were 749 new GMMs for labeling the frames of the template representatives, there was an extra memory overhead of 5.4 MB.
In Table 4, we provide a comparison on the per-frame computational time for the proposed template-matching-based lattice rescoring and the HMM baseline. The computation time was divided into two parts. One part was on test-frame labeling which used GMMs from the HMM baseline and the time was proportional to the total number of GMMs extracted from the HMMs. The other part was the rescoring time which calculated the DTW matching scores between a test segment (time marked by a phone arc on the phone lattice) and templates in a template clusters (specified by the PDTs of the phone unit). The more templates were in a template cluster, the longer the rescoring time. Since the KL distances between the GMMs were pre-calculated and the likelihood scores used in LLR distance were obtained in the test frame labeling, the time for rescoring was mainly consumed on determining the warping path in DTW, and hence for the LLR and KL distances, the rescoring times were similar (we therefore omit the local distance in Table 4). Relative to the decoding time per frame of the HMM baseline, when all templates were used, the test per-frame labeling overhead was 40% and the rescoring overhead was 22%, and hence the overall computational overhead per frame was 62.0%. In template selection, since only 20% template representatives were used, the rescoring time was reduced to 1/5 of the all-template case, and the computational overhead became 44.4%.In template compression, the number of new GMMs that were merged from the baseline GMMs was about 63% of the baseline GMMs (749 vs. 1,189), the time consumed for test frame labeling also decreased, and the computation overhead was reduced to 26.8%. Based on these numbers, we conclude that by using template representatives with a selection percentage of 20%, the costs in computation time and storage space were greatly reduced.
5.4 Large vocabulary speech recognition task
Based on the outcomes of the TIMIT phone recognition task, we only report the telehealth results for the following three cases of template matching: (1) all templates with LLR distance, (2) MLTS with KL distance, and (3) template compression with KL distance, where the cases 2 and 3 used 20% templates as the representatives and word accuracy was averaged over the five doctors. In template compression, the number of Gaussian components in each new GMM was 16, the same as the GMMs of the baseline; the average number of GMMs generated for the compressed template representatives was 1,048 per doctor (the baseline was 1,905 GMMs per doctor). The HMM baseline was trained using crossword triphone models, with an average word accuracy of 78.43%. In Table 5, we compare the recognition word accuracies between the HMM baseline and the template-based methods. In case 1, the average word accuracy was 80.03%, which is an absolute gain of 1.6% over the baseline. In case 2, the average word accuracy was 79.40%, which is an absolute gain of 0.97% over the baseline. In case 3, the word accuracy was 79.70%, which is an absolute gain of 1.27% over the baseline. Again, we conducted a Student's t test on the word accuracy gain (averaged over the five doctors) obtained by each of the three cases over the baseline and found the performance gain in every case to be statistically significant at the level of α = 0.05.
In Table 6, the average computation cost of the five doctors is given for the three cases. In comparison with the TIMIT phone recognition task, even though there were more GMMs to be used for test frame labeling and more templates in template clusters, the computation overhead did not increase much, especially for template selection and template compression. In addition, the memory overhead for all five doctors was around 236.2, 47.2, and 73.2 MB for using all templates, selected template representatives, and compressed template representatives, respectively. Therefore, the template-based methods, especially MLTS and template compression, are affordable for LVCSR.
5.5 Discussion
So far we have shown that representing the template frames by GMMs and using the local distance measures of LLR or KL significantly improved the accuracy performance over our HMM baselines, and the proposed methods are much more effective than the conventional template matching methods where the template frames use the original speech features. A question naturally arises is how would the proposed template-matching methods interact with an underlying acoustic model from which the GMMs are derived and the phone or word lattices are generated, and of particular interest is that as a baseline HMM system improves, whether the performance gain we have observed by the proposed template matching methods can still hold. This is a relevant issue since a baseline HMM system can be improved by using more advanced training methods and better features. Recently, a major advance has been made in using deep neural networks (DNNs) with many hidden layers for speech acoustic modeling, where the resulting DNNs learn a hierarchy of nonlinear feature detectors that can capture complex statistical patterns for speech data. For example, context-independent, pre-trained DNN/HMM hybrid architectures have achieved competitive performance in TIMIT phone recognition [38], context-dependent DNN/HMM has led to large improvements to several public domain large speech recognition tasks [39], and dumping features from deep convolutional neural networks to train GMM/HMM-based systems achieved higher accuracy performance than DNN/HMM hybrid architectures in several tasks [40].
We have investigated this issue in [41] on the TIMIT phone recognition task by performing lattice rescoring with the proposed template-matching methods on top of progressively better HMM baselines, where the test set was the same as discussed in Section 5.1. The HMM baseline system employed discriminative training, neural-network-derived phone posterior probability features, as well as ensemble acoustic models, etc. We observed that with the baseline system phone accuracy raised to 73.25%, 75.66%, 76.51%, and 77.97%, the template-matching-based lattice rescoring delivered consistent performance gains and gave phone accuracies of 74.74%, 77.27%, 77.96%, and 79.55%, respectively, where the phone accuracy of 79.55% was among the best reported results on the TIMIT continuous phoneme recognition task. For the sake of space, we omit the details of these baseline systems. For further information, please refer to [41]. The consistent performance gains support the notion that template matching improves recognition accuracy through a mechanism different from HMM. This is in agreement with the observation in [10] that the template matching system and the HMM system behave differently in word error patterns. Since our template-based methods are compatible with the GMMs trained from neural-network-derived features, it is reasonable to expect that our methods can take advantage of and add value to the advancements in this research direction.
6 Conclusions
In this paper, we have presented a novel approach of integrating template matching with statistical modeling for continuous speech recognition. The approach inherits the GMMs and the PDT state tying structures from the baseline HMMs and is therefore easily implemented. Generating template representatives and representing the frames by GMM indices make the approach extendable to LVCSR task. Based on our experimental results from the tasks of TIMIT phone recognition and telehealth LVCSR, we conclude that the proposed method of integrating template matching and statistical modeling has significantly improved the recognition performance over our HMM baselines, and the proposed template selection and compression methods have also largely saved computation time and memory space over using all templates with small losses in accuracy performance. Although in the current work we used the basic acoustic modeling techniques to train our HMM baselines, the proposed template matching methods can take advantage of and add value to more advanced GMM/HMM systems, and as such they are promising for further improving the state-of-the-art speech recognition.
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The work described in this paper was conducted during the first author's Ph.D. study at the University of Missouri-Columbia, USA.
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Sun, X., Zhao, Y. Integrated exemplar-based template matching and statistical modeling for continuous speech recognition. J AUDIO SPEECH MUSIC PROC. 2014, 4 (2014). https://doi.org/10.1186/1687-4722-2014-4
DOI: https://doi.org/10.1186/1687-4722-2014-4
- Gaussian mixture model
- Template matching
- KL divergence
- Dynamic time warping
- Large vocabulary continuous speech recognition


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COVID Alert NJ: Traditional Contact Tracing
What is traditional contact tracing and how does it work?
Contact tracing is a long-standing vital public health tool used in New Jersey and throughout the world to identify infected individuals and their close contacts to stop the spread of disease. It's been used for many contagious diseases, such as measles and HIV, and is now an essential tool in the fight against COVID-19. Contact tracing involves public health officials calling an infected individual, providing them with information / support and speaking with them about people they have had recent close contact with. The public health agency then reaches out to those close contacts to alert them of their exposure and to advise them on a course of action to minimize spread. The public health representative can also provide information on social services available such as food assistance and job protections if isolation is required. This process requires an army of public health representatives (contact tracers) – each call can be time consuming, and contact tracing can miss a number of individuals who are unknown to the infected case, such as someone in line behind you in the grocery store. For more information, please visit https://covid19.nj.gov/pages/testandtrace
How does traditional contact tracing interface with COVID Alert NJ?
COVID Alert NJ will augment traditional contact tracing, especially if it can be used in an automated and scalable way to help determine who has been exposed to a person that later reports a positive diagnosis of COVID-19. For example, the app can be used to send a rapid notification to the exposed person with instructions on next steps. These notifications may be beneficial by alerting an exposed individual faster than they would be notified via traditional contact tracing; they will enable public health authorities to contact and provide guidance to the individual and, where appropriate, include them in traditional contact tracing efforts. Additionally, if the positive COVID-19 individual doesn't know the names and contact information of everyone they have been in close contact with (for example, on the train commuting to work, at a café, or a social gathering), COVID Alert NJ helps reach those "unknown" contacts while keeping the positive COVID-19 individual and the contacts anonymous.


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One of the Foundation's earliest partners, National Bank has a particular interest in the University Awards of Merit, in support of which it contributes a significant amount each year. In 2020-2021, it granted the first University Award of Merit at the undergraduate level. In 2021-2022 it will offer the University Award of Merit at the postgraduate level, as well as giving out the second University Award of Merit at the undergraduate level. Its service offering to new engineers (and experienced ones) is also highly advantageous. Sponsor of the University Awards of Merit and presenter of our recipients testimonials.
Rio Tinto is a mining and metals company present in some 35 countries. Its mission consists of producing the materials that are essential for human progress. The company always goes above and beyond in terms of community support. This year, it joined forces with us to support college students. Brand new in 2021-2022, the Rio Tinto scholarships are aimed at college students who are planning a future in engineering. Five scholarships of $3,000 each will be granted during this first year of collaboration between the Foundation and Rio Tinto.
What if you could re-imagine today for tomorrow ?
At WSP, we are the sum of our collective passion, vision and expertise. We develop creative, comprehensive and sustainable engineering solutions for a future where society can thrive. Equipped with an intimate understanding of local intricacies, world-class talent and proactive leadership, we plan, design, manage and engineer long lasting and impactful solutions to uniquely complex problems.
A partner of the Foundation for many years, TD Insurance highlights excellence first through a generous contribution to the University Awards of Merit at the undergraduate, graduate and postgraduate levels. To help us recognize the contribution of all our recipients as well as our other partners, TD Insurance is also the presenting sponsor of the Pyramid Gala, the first edition of which was held virtually on May 28, 2021. TD Insurance also develops advantageous insurance solutions for engineers.
Well-known among engineers in Quebec, Gestion Férique has supported the Foundation for many years now by awarding the 3rd University Award of Merit at the undergraduate level. Gestion Férique is a non-profit organization whose mission is to contribute to the financial health of engineers and those with engineering degrees. If you want to accelerate your financial independence, keep this partner in mind.
Every year since 2018, CIMA+ has offered substantial financial support that allows us to help a good thirty engineering students stay the course to success. In three years, some 100 young people representing the engineers of tomorrow have benefited from the support of CIMA+. As well as being a Major Partner of our New Generation Scholarships, CIMA+, one of the largest private consulting engineering firms in Canada, with more than 2,400 employees spread over more than 30 offices from Atlantic to Pacific, also created the CIMA+ Scholarship, given to a future engineer who has distinguished themselves through their creativity and interest in sustainable development.
Present across the globe and counting more than 40,000 employees worldwide, SNC-Lavalin has chosen to get involved alongside the Foundation to showcase the leadership and excellence of a person who, while studying engineering, has made a clear difference by means of their leadership qualities. With that goal in mind, the SNC-Lavalin Leadership Scholarship was born. This annual scholarship of $10,000 is the largest ever offered by the Foundation. To meet the needs of the numerous projects it carries out, SNC-Lavalin is continually recruiting. Future engineers take note…
Pomerleau's history is, first and foremost, one of strong family values and a solid work ethic, infused with an entrepreneurial spirit. It's a story of growth and evolution, propelling this little company from Beauce, QC, into the coast-to-coast business it has become.
Today, Pomerleau supports future engineers in a special way with The Pomerleau Award of Merit for Entrepreneurship.
Ultra Communications supplies critical communications products for defense and public security systems. Offering robust wireless broadband communications, Ultra's solutions can be integrated into a variety of critical communications architectures and put to work in any deployment scenario. It's with great pride that the Foundation has worked to create a scholarship spread over three years aimed at young women wanting to pursue studies in engineering: the Women in Engineering Scholarship, which will mark its third anniversary in 2021-2022.
We are proud to count Polytechnique Montréal among our key partners. A renowned engineering university and international caliber scientific and technological centre, Polytechnique Montréal has partnered with us since 2013, in recognition of our contribution to the support and advancement of students in Quebec hoping to become engineers.
Initiator of the planned giving program at the Foundation, Pierre Sauvé also created the Pierre Sauvé, Eng. Scholarship for the promotion of women in engineering. Since 2019, this scholarship has rewarded the efforts of an undergraduate student in engineering who has made a remarkable effort to promote the profession among girls.
The Coup de cœur SOPREMA Scholarship is awarded to a college student who dreams of engineering and plans to enroll in this field of study as an undergraduate. The SOPREMA scholarship is open to CEGEP students in a pre university program and those in a civil engineering technical program.
Founded in 1999, Laporte is a company based in North America and Europe whose mission is to provide high value-added engineering service to pharmaceutical, agrifood and bioindustrial plants.
A major donor since 2020, we have Gilles Gauthier, Eng. to thank for the implementation of the gift of shares to the Ordre des ingénieurs du Québec Foundation.


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Dumb Ways To Die: Party
Tags: Music Games / Funny Games / Kids Games / Boy Games / Rhythm Games / html5 Games
Video Walkthrough
Our tiny colored pals are back again for this amusing response game, Dumb Ways To Die: Party. Touch the circles in time to entertain the group and make points. If you miss the notes, the funny dancing characters will burn in flames and the crowd will begin booing, so do your greatest to maintain this party rocking! Have fun!
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Reid Youngdahl is a Valve Technical Specialist at Emerson's flow controls products in Marshalltown, Iowa. He began working for Emerson in 2013 as an applications engineer, providing global control valve technical support for various industrial markets, such as oil and gas, power generation and OEMs. Mr. Youngdahl provides technical consultations for severe-service control valve applications to optimize customer solutions. He holds a BS degree in mechanical engineering from Iowa State University.
In today's business climate, where project budgets and schedules are under cost and time pressures (and where installed valve applications must perform reliably under increasingly severe conditions), valve simulations are enabling new capabilities.


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Below is a list of the types of businesses in the City of Lanai City in which we have business listings. If you do not see your city within the list below. You can add a business for just $49.95 per year. To add a business submit your info here.
Lanai City is located in the island of Lanai and is a part of Maui County in the U.S. state of Hawaii. Lanai City is also the commercial hub of the island. A large number of business establishments are located in the city. The education sector is also quite well established. Lanai City has a total area of about 7 sq. miles. Most of the residents are engaged with shops and restaurants. There is also a historic district, which attracts a number of tourists in the area. Lanai City also has a airport in its vicinity.


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Drug Treatment & Rehab Centers in Fredericksburg, IA
Pathways Behavioral Services Inc
Pathways Behavioral Services Inc is an addiction treatment center based at 602 South Washington Avenue in Fredericksburg, Iowa.
The major treatment approaches at this center include Behavioral Therapy, Relapse Prevention Programs, 12-Step Support Group Therapy Approach. The program also provides their services in different environments, for instance Outpatient Treatment.
Pathways Behavioral Services Inc also offers a number of programs that are tailored to meet the needs of specific clients such as: Addiction Treatment for Teens, Addiction Treatment for Women, Drug Rehab for Adult Men. Finally, Pathways Behavioral Services Inc accepts various forms of payment, such as Self-Pay, Medicaid for Alcohol and Drug Treatment, Private Insurance Coverage.
We can help you find the right treatment facility that best fits your overall needs and financial requirements.


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Hayter, A.J., Wynn, Henry P. and Liu, W. (2004) Confidence bands for regression: the independence point method. S3RI Methodology Working Papers (M05/17). Southampton Statistical Sciences Research Institute, Southampton, UK.Full text not available from this repository.
In some circumstances for a linear model with ? parameters ? regression in Rd of the form ? one can find special points ? for which the usual least squares estimators ? of the expected response ? are uncorrelated, independent in the Gaussian case. Following Wynn (Biometrika, 1984) we use this to set up simple piecewise linear confidence bands in the case ?, namely the additive main effect model in multiple regression and some other cases.
|Item Type:
|Monograph (Report)
|Official URL:
|Additional Information:
|© 2004 The Authors
|Centre for Analysis of Time Series
|Q Science > QA Mathematics
|Date Deposited:
|26 Feb 2014 16:04
|Last Modified:
|24 Aug 2021 23:06
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How Big, How Blue, How Beautiful
June 2, 2015 § Leave a comment
It is 55 and drizzly in New York and thank heavens, Florence and the Machine have released their third record: How Big, How Blue, How, Beautiful. And my initial, totally unprofessional assessment is that it is great. Early favorites include "Ship to Wreck", "Third Eye" (infectious), and "What Kind of Man". While I patiently wait for the new Frank Ocean and need a little break from Kendrick Lamar's latest (while excellent), this is just the ticket!
Lovely To See You…
September 12, 2013 § Leave a comment
Indeed! Alison Goldfrapp gave a fantastic performance at the Beacon on Tuesday night and I was so glad that we were able to attend. She was accompanied by her musical partner, Will Gregory, their band and The Wordless Music Orchestra. But this was not a "Goldfrapp Unplugged" affair.
Using everything at their disposal, including a well-edited set list that covered most of their beautiful new record, Tales of Us, along with more moody but equally lovely songs (mostly from Seventh Tree and Felt Mountain) they heightened each song's intent, without it ever feeling like "I am so tired of performing this song, we are totally reworking it".* The result: an exhilarating show that made me reconsider those that weren't necessarily my favorites and revel in the ones that I loved already.
I've never seen them live before, and was completely taken with Ms. Goldfrapp's voice — no backup tracks here! I also loved how she managed to be fun and a touch saucy with the audience, while also seeming the teeniest bit shy. (Until she was consumed with the music — then there were moments of absolute fierceness. This is a woman I'd like to have a scotch with…or maybe two.)
Did I mention that she looked like what I imagine a really cool angel might look like? She did.
* In my totally unprofessional opinion.
(Photo that no one said was prohibited: Mr. H)
One Last Hurrah…
August 29, 2013 § 2 Comments
Oh my goodness! How did it get to be the beginning of Labor Day Weekend? (AKA: The End of Summer.) I know that it was busy, but really, this one went way too fast. Thankfully, I think we made the most of it, especially over these last couple of weeks. So, before we start thinking about fall and Fashion Week and All That Work We Need to Do, some last bits of summer, which in my book is at its best by the beach…
One of my favorite things about the season is the flowers — I know, this is not new information. And I especially love these particular roses when they greet me each day…
The Village Improvement Society's Annual Geranium Planting (I am not kidding about this, even though it sounds very Star's Hollow) makes dog walks* a little bit more inviting in the morning, when I pretty much don't want to be walking at all.
Also making dog walks and other activities more enjoyable is our town itself, which I find to be quite charming. Pierre's is our year-round spot for breakfast, lunch and dinner (and the best fries around), and the library next door is very high on the Adorable Factor, as is their library card, of which I am a proud holder. (But I am pretty sure I owe them a late fee for a Mary McCarthy biography. I feel bad about that…I really do. But somehow, I still haven't made it back in to pay it. I think it is shame that is keeping me away.)
And then there is the beach. I don't think there is a better place to take a nap than on a beach. And nap I did this summer. Quite a bit. This was my typical view when I woke up from one of said naps…
Unless I was opted for the shade of our beach camp…
And sometimes, after napping, walking would happen. I know, everyone says it, but that is because it is true: A walk on the beach clears the mind, lifts the spirits and sometimes gives you ideas for interior design projects. (OK, that last part is probably just me.)
(And this shell below has me thinking of all sorts of masculine colors for our kitchen project….but that is for another post.)
Then there is eating. One of my favorite things to do. Ina always talks about them, but this place really is yummy…
As is Navy Beach, which has been GOOP-ed, but I like to think that I knew about it first…
If only they were a little more forthcoming about the availability of tables outside…
Not that I am complaining. Well, actually, I am. Seriously, that was just a few of the open tables outside. Grrr.
Anyway, one thing I could never complain about is Mr. H's annual Birthday Dinner with our dearest friends. As always, this was one was excellent — and included doughnuts from Dough!
Naturally, this kind of eating requires a little more movement than just rolling over on my beach towel. So, I did attempt a jog or two and either I hallucinated it, or there is an amazing cobweb in this photo that I cannot see. If you can, I am very happy for you.
And just in case you were concerned, A+O also had a very restful summer, including lots of time on the down cushions of our sofa. This does not please me, but they are so darn cute that I let it slide…
And this year, we had an extra fun treat in the form of a fantastic sail on the most lovely boat…
I am not a sailor, but I think this one is pretty "yar". (Watch The Philadelphia Story, please.)
So, even though I hate to see it go, I think that we did summer quite well this year, and hope you did same. And don't forget to savor this long weekend — whatever your plans may be. I will probably still be napping and eating, if only for the sake of consistency.
* In all fairness, Mr. H performs this duty far more frequently than I do. But, I am the one on teeth brushing and managing their schedules. Yes, they have schedules because parts of my life are ridiculous.
(Photos: Some Cozy Night and Mr. H)
On the Tube
March 22, 2013 § 2 Comments
Not the subway in London. I am writing of the television. Or iPad or other tablet that you may use to watch programming. (I've been reading legal documents, so please forgive the halting language.)
While I patiently wait for the return of Mad Men, followed by Breaking Bad's last few episodes*, I have been thoroughly enjoying FX's The Americans. I never watched Felicity, but will admit to being wary of Keri Russell playing a deep cover agent for the KGB during the cold war. But I am delighted to say that I was an idiot. She is fantastic. (And soooo tiny to kick so much a**!!) As is Matthew Rhys as her KGB Husband, who astounds me once again by his flawless American accent despite being Welsh**.
I'm not sure how best to describe this show. It has the slow burn of Mad Men but doesn't over-use the time period to make any points. I suppose it is mostly about relationships, trust and loyalty, and what happens when those things don't all fall into place so neatly. It is also about SPIES, so it is full of excellent fight scenes, "missions", gadgets and disguises. There is a decent mount of violence and tons of sex (which could be toned down in my opinion), but the plot lines and characters are so consuming that it never feels as if that is the point of the show. So, if you are looking for something new to watch that isn't mindless, give this one a shot.
_.._ _.._ ***
* What will Hank do??? Will Jesse be ok? And what leads Walter to that first scene of last season's opening? God, I am going to miss this show.
** The one accent possibly more impenetrable than the Scottish accent. And I have recent experience: My new hair stylist is Scottish and I seriously have no idea what we discussed during most of my first visit with him. Thankfully, I love the cut, which includes bangs…for another post.
*** Yes, that is morse code for "xx"
Getting Into the Spirit: Film Edition
December 17, 2012 § Leave a comment
Hopefully you are nearing the end of all the prep work and almost ready to settle in for a little Holiday R&R. And if you aren't, you probably need a break. So here are some movies that I can't help but watch this time of year, often by myself, as they range from weird to sappy…
Desk Set, 1957
I love this movie. LOVE IT! Katherine Hepburn, Spencer Tracey and The Electronic Brain (also known as EMERAC). It isn't about the holidays, but enough of it takes place during that time of year so that it feels right. My favorite scene is the dinner at Hepburn's apartment, which is completely cozy and strangely chic. And without this film, I would never be able to ask Mr. H "How do you like your chicken fried?" when we obviously can't understand a word either of us are saying. (Just watch it and that will all make sense.) Adorable and fun. And you can refer to your computer as "The Electronic Brain" for at least 24 hours after viewing before people get annoyed with you.
Little Women, 1994 (A.K.A. "The One with Winona Ryder")
What can I say? This one reeks of Christmas and Simpler Times and The Goodness of People — an explosion of horse-drawn coaches, kittens, quilts, snow and flowers. And Marmee is played by Susan Sarandon, who was pretty much The Best Marmee Ever. Have I mentioned that Christian Bale is in it? Yes, he is, which provides a decent enough distraction from Winona Ryder, who, let's face it, tends to be flat. Then there is the Beth Death Scene, which is wonderfully sappy and leads perfectly into the happy ending. For the ladies, only, I think. And make sure you have a nice, soft hankie.
White Christmas, 1954
True, it makes no sense but it is cheery and has "Christmas" in the title. And I really love singing along when the girls do their "Sisters" number. I really do.
Rosemary's Baby, 1968
I know what you are thinking: What?? Well, all I can say is that I love this movie and will take any excuse to watch it. Aside from the insanity of it all, I love the amazing apartment, every single Rosemary Woodhouse outfit and the fact that this appears to have been made in a serious manner without anyone thinking that the plot is completely nuts. Campy good fun.
When Harry Met Sally, 1989
You know why. And I believe it to be an American Classic.
Happy Viewing!
(Photo: Some Cozy Night)
channel ORANGE
September 17, 2012 § Leave a comment
Do you love classic Stevie Wonder? Dig a little early Prince…and maybe even a dash of Lenny Kravitz before you discovered the others? Then Frank Ocean's "channel ORANGE" may be for you. I've been hearing about this record all summer long and finally downloaded it from iTunes this weekend. At its best (in my non-professional opinion), it is intelligent and funny, and in between it is kind of romantic. Mr. H and I followed it up with some Fitz & the Tantrums and then a little Spinners on our way home last night, which turns out to be a good antidote to traffic and nutty drivers. I recommend!
Have I Mentioned??
August 31, 2012 § Leave a comment
It's been some time I've noted things I've been loving lately. So just in time for you to ponder "Where has the summer gone?" and "Ack! Am I ready for fall?" this weekend, some more food for thought…
1) Vogue.com
While the magazine isn't always as fashion forward as the cooler kids (V, for instance), I am still a Vogue Girl. And their site, while not quite well-formatted, provides bursts of inspiration any time you need it. I especially like APT with LSD, the Best Dressed of the Week and all their thoughts on things to do at a given time of year. Enjoy! (http://www.vogue.com/)
2) Yoga Toes
During a recent trip to southern California with some girlfriends, we discussed the important subject of keeping our feet in good shape as the years pass by. And that is when Yoga Toes came up. They are a touch hard to explain, but I have been using them for about ten minutes a day for well over a month now, and I love them. A great stretch for your tootsies that have been shoved into all manner of foot-hostile shoes. And don't fret, they come in clear. (http://www.yogapro.com/products/YogaToes.html?utm_source=adCenter&utm_medium=cpc&utm_campaign=YogaToes%2BKeywords%2B-%2BUSA)
3) Diptyques's Mini Candles
As I have said before, Diptyque makes the best (outrageously priced) scented candles on the market. Full stop. And their mini ones are great for trying out new scents, to give as gifts and to just save a few dollars if you haven't been fully converted yet. I am especially loving the
Feuille de Lavande scent, but there are few that I don't adore. (http://www.diptyqueparis.com/home-fragrances/mini-candles.html)
For the major splurge, nothing feels more right these days that Phoebe Philo's chic, streamlined and wearable designs. I loved her work with Stella McCartney and at Chloe (much of which I still have!), but she is just white hot these days. No gimmicks. Not super trendy, despite everyone in fashion loving everything she is doing right now. Just things that will effortlessly fall into your wardrobe and make you feel confident and comfortable. Love. (http://www.celine.com/)
5) Ciao Bella Malt Ball Gelatto
Theis flavor has been around for a while, but in case you are unfamiliar with it, put down your computer and go get yourself a container. There really isn't anything more to say on the matter. (http://ciaobellagelato.com/#/products)
6) Tokyo Banana
Friends of mine brought me a box from a recent trip to Tokyo. Apparently, they are kind of a touristy thing to buy, but they are like a Japanese Twinkie and they are also crazy delicious. (http://www.tokyobanana.jp/)
And that is all I have got for you this evening. Enjoy and have a great long weekend!
(Funky photo of the bag my latest splurge came in, taken while on a bus, mind you: Some Cozy Night)
Lazy Saturday
March 17, 2012 § Leave a comment
So, it is a beautiful day here by the beach and I did manage to make some delicious four grain pancakes for breakfast and take a ton of photos of the real signs of spring's arrival, but I haven't felt like doing much else. This is a good thing, as I have a nice pile of books to get through…
And some willing participants in General Loafing.
Hope your Saturday is a relaxing one. And I promise a proper post soon.
(Photos of my book pile and A+O at rest: Some Cozy Night)
The Perfect Excuse
January 21, 2012 § Leave a comment
Happy Saturday! I stumbled out of bed after a 36-hour bug to find this outside my window:
"Did it snow?" I asked myself. I checked again. Yes, it had. Even though the weather report had not indicated that we would be getting more than an inch or two and I was not convinced we would get any. For the record, we definitely got more than and inch or two.
I surveyed my options and while the thought of building a snowman crossed my mind, after assessing the accumulation a little more closely while out with A+O, I decided my best course of action was a day indoors. A perfect excuse to catch up on some reading…
I have been meaning to get to this Jansen book for well over a year. And it is fascinating. (As is Know Your Rodent, but that is a much quicker read.) While reading about the many amazing homes this venerated design firm decorated well into the later part of the 20th century, I began to get The Craving to order a few more books on some of the more infamous and glamourous figures who made up much of Jansen's clientele and their circle, including the Mitford sisters and Nancy Lancaster. It seems to me that these topics could take me through a number of other snowy days and it is totally possible that this weather is here for a spell:
Really, who wants to go out in that, which isn't even as bad as it could get. Much better to be curled up with a book and a dog, I say.
So, I fixed the fire…
Which enabled Mr. H to build upon said fire…
Leaving me with nothing to do but order my books, keep reading and then make dinner for friends*, starring a fresh bolognese from Marcella Hazan, who is pretty much that last word in these matters.
So, it will have been basically a day of loafing and pasta-eating, which we all need from time to time. So, thank you very much Surprise Snowfall.
*Although even that might be a Mr. H production. At least I set the table. And pulled out the necessary ingredients.
(Photos: Some Cozy Night, who left her good camera in the city.)