Abstract
Abstract This work describes the recognition of phonetic Arabic figures. Speech recognition technology has made steady progress in its 50 years history and has succeeded in creating several substantial applications. The goal of speech recognition research is to produce a machine which will recognize accurately the normal human speech from any speaker.
Abstract
To improve the performance of recognition system, an effective and robust technique is proposed to extract speech feature. The input speech signal is decomposed into various frequency channels based on time-frequency multi-resolution property of wavelet analysis. For capturing the characteristics of the signal, the Mel-Frequency Cepstrum Coefficients ''MFCCs'' of the wavelet channels are calculated. Hidden Markov Models ''HMMs'' were used for the recognition stage. Different forms of wavelet functions were used to evaluate the best wavelet signal to extract the best features of the signals. It is found that the wavelet signal ''db8'' gives the highest values of recognition accuracy rate. A recognition rate of 98% was obtained using the proposed feature extraction technique.
Abstract
A comparison between different features of speech is given. The features based on the Cepstrum give accuracy of 94% for speech recognition while the features based on the short time energy in time domain give accuracy of 92%. The features based on formant frequencies give accuracy of 95.5%. It is clear that the features based on MFCCs with accuracy of 98% give the best accuracy rate. So the features depend on MFCCs with HMMs may be recommended for recognition of the spoken Arabic digits. Center.
Introduction
Automatic Speech Recognition (ASR) is a technology that allows a computer to identify the words that a person speaks into a microphone or telephone. It has a wide area of applications: Command recognition (Voice user interface with the computer) and Dictation. ASR can help handicapped people to interact with the society. Selection of speech features is very effective in recognition accuracy. Most application areas of speech technology, including speech recognition, speech synthesis, speech coding, and speaker recognition require some form of preliminary analysis of speech. Speech analysis techniques may be broadly classified as either frequency domain or time domain approaches. Wavelet transform (WT) is a transform that provides time-frequency representation of the signal. Speech recognition is the process of automatically recognizing the spoken word based on information included in the speech wave. Any utterance contains information about the word being spoken and also about the identity of the speaker. In speech recognition system, we wish to select the first type of feature and ignore the second while in speaker recognition; we wish to do just the opposite.
Introduction
The most important parts of a recognition system are the feature extraction techniques and the recognition methods. The feature extraction step converts the properties of the signal which are important and effective for pattern recognition task which simplifies the distinction of the classes. The recognition step aims to estimate the general extension of the classes within feature space from training set [1, 2] . For speech feature extraction, Linear Predictive Coding (LPC) of speech has proved to be a valid way to compress the spectral envelope in all -pole mode [1, 2] . The short time energy function of speech may be computed by splitting the speech signal into frames of N samples and computing the total squared values of the signal samples in each frame. This can be achieved by multiplying the signal by a suitable window function [3] . The energy per frame can be considered as a feature for speech signal. There are many techniques to extract the speech feature. Features are usually selected intuitively [4, 5] . After features are selected, we must try them out and see how they separate the different classes. Statistical methods and in particular Hidden Markov Models are used for recognition step in this work.
Introduction
In this paper, a robust feature extraction algorithm for speech signals is proposed. This algorithm depends on combining both the wavelet transform and the MFCCs for the feature extraction stage. First, the wavelet transform is applied to decompose the speech signal into two different frequency channels. The components of the low frequency channel are the approximations, while the high frequency channel components are the details. The decomposition process can be iterated with successive approximations being decomposed. For capturing the characteristics of the individual speakers, the FCCs of the approximations and detail channels are calculated. Based on this mechanism, the multi-resolution features of the speech signal can easily be extracted using the wavelet composition and calculating the related coefficients. HMMs are used for identification stage.
Wavelet-based MFCCs feature extraction technique for speech
Speech is a complicated signal produced as a result of several transformations occurring at several different levels: semantic, linguistic, articulator and acoustic. Differences in these transformations appear as differences in the acoustic properties of the speech signal [5] . An important problem in speech recognition systems is to determine a representation that is well adapted for extracting information content of speech signals.
Wavelet-based MFCCs feature extraction technique for speech
In general, transforming a signal to a different domain is done to get a better representation of the signal. For recognition, better means having more ability to separate signals which belong to separate classes or categories in the new domain than in the original domain.
Wavelet transform
Wavelet analysis is a mathematical discipline which has generated much interest in both theoretical and applied mathematics over the past decade. Wavelets have the ability to analyze different parts of a signal at different scales. The wavelet transform (WT) is a transformation that provides time-frequency representation of the signal. The continuous one dimensional WT is a decomposition of f(t) into a set of basis function W a,b (t) called wavelets [6] :
Wavelet transform
The wavelets are generated from a single mother wavelet by dilation and translation
Wavelet transform
where: f(t) is the signal to be analyzed, a is the scale, and b is the translation factor. W(t) is the transforming function and is called the mother wavelet. Filters of different cutoff frequencies are used to analyze the signal. A continuous wavelet transform can operate at every scale. Also the analyzing wavelet is shifted smoothly over the full domain of the analyzed function. In discrete wavelet transform (DWT), scales and positions of powers of two are chosen. An efficient way to implement this scheme using filters was developed by Mallatin [7] . Given a signal S of length N, the DWT consists of log 2 N rochakyle@example.org. The first step produces, starting from S, two sets of coefficients: approximation coefficients CA1 and the detail coefficients CD1. These vectors are obtained by convolving S with a low pass filter for approximations, and with a high pass filter for details, followed by dyadic decimation. The next step splits the approximation coefficients CA1 into two parts using the same scheme, replacing S by CA1 and producing CA2 and CD2, and so on. This technique is most effective when it is applied to the detection of short-time phenomena, discontinuities, or abrupt changes in the signal.
Mel-Frequency Cepestral Coefficients (MFCCs)
The Cepstrum of a signal is the Fourier transform of the logarithm of its power spectrum. Let X(x) denotes the spectrum of the voiced speech signal, P(x) denotes the spectrum of the pitch impulses and H(x) denotes the spectrum of the vocal tract which includes the effects of glottal wave form. The relation between the magnitudes of these three spectra can be expressed simply as follows [8] :
Mel-Frequency Cepestral Coefficients (MFCCs)
Taking logarithm of this equation gives:
Mel-Frequency Cepestral Coefficients (MFCCs)
Thus, in the logarithm of | X(x) |, the contributions due to P(x) and H(x) are added. The contribution from H(x), which is essentially determined by the properties of vocal tract itself, tends to vary slowly with frequency, while the contribution from P(x) tends to vary more rapidly and periodically with frequency. These two components should be separated by means of a linear filtering operation. Removing the pitch ripple from Eq. (4) leaves only the vocal tract transfer function. Taking the Fourier transform of it, Cepstrum can be obtained.
Mel-Frequency Cepestral Coefficients (MFCCs)
In sound processing, the Mel-Frequency Cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear Mel scale of frequency. MFCCs are coefficients that collectively make up an MFC. The difference between the cepstrum and the Mel-Frequency Cepstrum is that in the MFC, the frequency bands are equally spaced on the Mel scale, which approximates the human auditory system's response more closely than the linearly-spaced frequency bands used in the normal cepstrum. For speech recognition, the mel spectrum is used to reflect the perception characteristics of the human ear. This frequency warping can allow for better representation of sound [8] . The first step is to divide the speech signal into blocks using overlapping smooth windows such as Hamming, Hanning, etc. The next step is to take the Discrete Time Fourier Transform (DTFT) of the windowed signal. Next, the square of the DTFT of the windowed signal is calculated. The outputs of the fourth step are the mel-scaled filter bank energies. The fifth step involves calculating the logarithm of the mel-scaled filter bank energies. The last step involves taking the Discrete Cosine transform (DCT) of the mel-scaled log-filter bank energies to calculate MFCCs.
Wavelet based MFCCs feature extraction technique
In this the paper, the multi-resolution decomposing technique using wavelet transform is used [9] . Based on this technique, one can decompose the speech signal into different resolution levels. The characteristics of multiple frequency channels and any change in the smoothness of the signal can be then detected to perfectly represent the signals. Then, the MFCCs are applied to the wavelet channels to extract features' characteristics. MFCCs as previously stated, has the advantage that they can represent sound signals in an efficient way because of the frequency warping property. In this way, the advantage of both techniques is combined in the proposed technique.
Time normalization
By time normalization, it is meant the process where timevarying features within the words are brought into line. The classical technique is simply to stretch or compress the unknown word uniformly until it attains the same length as the reference. This process depends for its accuracy on accurate end point identification. Time normalization is now frequently done by a process known as time warping [9] . Time warping technique is used in this work for time normalization for spoken digits.
End point
The first step in recognition or identification is the problem of endpoint alignment (determining the location of the spoken word during the recording interval). The algorithm used to determine the endpoint has been described by Rabiner and Sambur [10] . The end region is defined as the region from the end of the word to the point at which the energy first exceeds 10% of the maximum energy. Equivalently, an initial region is defined from the beginning of the word to the point at which the energy first exceeds 10% of the maximum. The remaining section is termed the middle region. The middle region is the desired signal.
Word recognition using HMMs
The word to be recognized is suitably the endpoint, split into a time sequence of T frames and analyzed using wavelet analysis and Cepstrum. This produces a sequence of observation:
Word recognition using HMMs
We further assume that the sequence of observations may be modeled as a probabilistic function of an underlying Markov model [10] . The hidden Markov, M, is characterized by i. N = the number of states in the model. ii. M = the number of output symbols. iii. A = a ij , the transition matrix of the underling Markov chain. iv. B = {B jk } = {b jk }, the model output symbol probability matrix, where b jk is the probability of output symbol k given that the model is in state j. v. p = p i , i = 1, 2,. . .. . ..., N the initial state probability vector.
Word recognition using HMMs
The likelihood of each model M i having produced the observation sequence O t is obtained by computing Pr i {(O t /M i } that is the probability of the observation sequence O t given model M i . The recognition word (RW) is then given by [11] . 
Word recognition using HMMs
The probability of emitting the entire observation sequence of length T and being in state j is a T (j). The total probability of emitting the observation sequence, Pr{O/M}, may be obtained by summing a T (j) across all N states. When Pr(O/M) is computed in this way it is referred to as the Baum-Welch probability PBW and is given by [11] : 
Word recognition using HMMs
Taking into account the observation O t + 1 produced by state j at time t + 1, then the recursive equation for forward probability a t+1 (j) is given by [11] :
Word recognition using HMMs
where b j (O t+1 ) is the probability of producing observation O t+1 from state j and is obtained from pattern probability
Word recognition using HMMs
is vector quantized to pattern PK. The recursion in Eq. (9) is initialized by computing a 1 (j) which is the probability of producing the first observation and being in state j at time t = 1, that is a 1 ðjÞ ¼ pfjgb j ðO 1 Þ ð 10Þ
Results and discussion
A phonetic recognition system is implemented to recognize phonetic Arabic digit spoken by any speaker (male, female). The scheme of the work is shown in Fig. 1 .
Results and discussion
The acoustic signal of the Arabic digit is received by microphone. This signal is analyzed by a wavelet and MFCC techniques then coefficients for each digit are produced. A hidden Markov model ''HMM'' is used to recognize each Arabic digit from its characterized coefficients.
Results and discussion
Speech recognition system consists of two phases:
Results and discussion
(1) Learning (training): It is the compilation of the library of reference patterns. (2) Recognition: It is the use of the completed library with unknown inputs.
Results and discussion
In learning, data for known phonetic Arabic figures are fed into the system and the system forms a reference pattern or template for each digit. In recognition, the system computes the pattern of features for unknown input and identifies the input with the spoken figure whose reference pattern matches these features most closely.
Results and discussion
The basic points of speech recognition:
Results and discussion
(1) Feature extraction.
Results and discussion
(2) Feature matching.
Results and discussion
Feature extraction is the process that extracts a small amount of data from the signal. Feature matching involves the actual procedure to identify the voice by comparing extracted features from input voice.
Results and discussion
In this work, a system is based on wavelet analysis that is used to analyze and extract features (as wavelet coefficients) from independent speaker. Different wavelet functions have been used to evaluate the best wavelet function corresponding to the high recognition rate. The wavelet functions ''db2'', ''db4'', ''db6'', ''db8,'' and ''db10'' are tested.
Database
The database used in this work contains the speech data files of ten speakers, five males and five females. It has the following features:
Database
The speech files consist of Arabic digit from zero to nine.
Speech Arabic Digit
Arabic Digit Speech Recognition Fig. 1 Block diagram of proposed Arabic digit recognition system. Fig. 2 A sample speech signal of zero digits from female.
Speech Arabic Digit
Each speaker repeats each digit 5 times, 4 of the utterances are for training and one for testing. The speakers are mainly from 20 to 30 years old. The data were recorded using a microphone, and all samples are stored in Microsoft wave format files with 22050 Hz sampling rate, 16 bit PCM and mono channel. Fig. 2 shows sample speech signal of zero digit from female. Fig. 3 shows sample speech signal of zero digit from male.
Speech Arabic Digit
Feature selection for Arabic digit recognition based on MFCC and wavelet
Speech Arabic Digit
Step 1: Reading the recorded wave file from MATLAB work directory
Speech Arabic Digit
Step 2: Performing decomposition at level 3 of the signal using db8.
Speech Arabic Digit
Step 3: Computing the coefficients (approximations and details) using wavelet technique
Speech Arabic Digit
Step 4: Applying MFCC technique to get the final coefficients. This is repeated for the different wavelet functions.
Applying the hidden Markov model (HMM) to recognize Arabic digit Building HMMs
HMM is modeling a stochastic process defined by a set of states and a number of mixtures per state. In this work, a recognition system is developed using HMM toolbox for use with Matlab. The models are built as follows:
Applying the hidden Markov model (HMM) to recognize Arabic digit Building HMMs
4 states left-to-right transition model is built for each digit. Each state of the HMM models a certain segment of the vector sequence of the utterance and the transition from one state to another describes how the process changes its characteristics on time. The probability distribution on each state is modeled using 8 mixtures of the state, the wide variability of the emitted vectors is allowed. This variability is caused by the variability of the characteristic of the speech signals. In choosing the number of mixtures, a compromise is made between the granularity of the modeling and the reliability of the estimation of the parameters. High number of mixtures means that a high number of parameters have to be estimated. The type of the covariance matrix chosen is diagonal. To reduce the number of computational efforts, it is often assumed that the individual features of the feature vector are not correlated. This reduces the number of computational efforts [12] .
Training HMMs
In the training stage, the observation sequences are used to adjust the model parameters to derive the maximum likelihood estimate of these parameters. The parameters to be estimated are:
Training HMMs
The initial states probabilities p i . The coefficients of the states' transition probabilities a ij . The mixture coefficients C jm . Mean vector for the mixtures' components l jm . Covariance matrices for the mixtures' components U jm .
Training HMMs
HMMs are trained using the Baum-Welch algorithm. This algorithm is used to derive the maximum likelihood estimate of the parameters of the HMMs given a dataset of output sequences using an iterative procedure. It works by guessing initial parameter values, then estimating the likelihood of the data using the current parameters. These likelihood values are then used to re-estimate the parameters iteratively until a local maximum is reached. Fig. 4 shows how the likelihood value is increased during the iterations of the algorithm.
Training HMMs
In order to perform Arabic digit identification using HMMs, a model is built for each Arabic digit and the model parameters that optimize the likelihood of the training set observation vectors are estimated [12] . Fig. 3 A sample speech signal of zero digits from male. Fig. 4 The likelihood values during the iterations of the training process for the digit zero.
Testing stage
For each unknown Arabic digit that is to be recognized, features are extracted from wave file, followed by calculation of model likelihood of all models, and followed by the selection of the digit whose model likelihood is the highest.
Testing stage
Results and discussions Table 1 shows the results of different wavelet functions. It is clear that the wavelet function db8 is the best with recognition accuracy rate 98%. The wavelet function db2 has a recognition accuracy rate 97%.
Testing stage
The result given in reference [13] for Arabic figures recognition based on formant using neural network showed that the recognition rate accuracy of 95.5%. This result was for an improved technique based on grouping the figures. The proposed system proved that it is better than the system of reference [13] . The proposed system accuracy rate is found to be 98% with ''db8'' function while it is 95.5% for system of reference [13] .
Testing stage
The accuracy is found to be 92% for feature based on short time energy in the time domain. The features based on the Cepstrum give accuracy of 94% while it is found to be 95% for feature based on linear prediction code [14] .
Testing stage
So, the features depend on MFCCs may be recommended for speech recognition where its accuracy is found to be 98% with the hidden Markov model as a classifier. Table 2 shows the results of different speech recognition techniques and the accuracy for each feature.
Conclusion
An effective and robust feature extraction technique is presented for deployment with speech recognition systems. Based on the time-frequency analysis of the wavelet transform, approximations and detail resolution channels are obtained. The MFCCs of the wavelet channels are calculated for capturing the characteristics of the speech signals. HMMs were used for the recognition stage. HMM for each figure is obtained with its parameters. After training to get the models, the test is introduced. Different wavelet functions were tested to evaluate the best function for feature extraction. The feature based on MFCCs is recommended for Arabic figures recognition with Hidden Markov models as classifier. It is found that the proposed system (the system based on MFCCs) gives recognition accuracy rate of 98% which is the highest recognition accuracy rate. It is found that ''db8'' is the best function. The recognition accuracy rate is found to be 98% with ''db8'' wavelet function. A comparison between different features of speech is given. The features based on the Cepstrum give accuracy of 94% for speech recognition while the features based on the short time energy in time domain give accuracy of 92%. The features based on formant frequencies give accuracy of 95.5%. It is clear that the features based on MFCCs with accuracy of 98% give the best accuracy rate. 

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