Mismatching problem between the source and target noisy corpora severely hinder the practical use of the machine-learning-based voice activity detection (VAD). In this paper, we try to address this problem in the transfer learning prospective. Transfer learning tries to find a common learning machine or a common feature subspace that is shared by both the source corpus and the target corpus. The denoising deep neural network is used as the learning machine. Three transfer techniques, which aim to learn common feature representations, are used for analysis. Experimental results demonstrate the effectiveness of the transfer learning schemes on the mismatch problem.
@cite_5: How to construct models for speech nonspeech discrimination is a crucial point for voice activity detectors (VADs). Semi-supervised learning is the most popular way for model construction in conventional VADs. In this correspondence, we propose an unsupervised learning framework to construct statistical models for VAD. This framework is realized by a sequential Gaussian mixture model. It comprises an initialization process and an updating process. At each subband, the GMM is firstly initialized using EM algorithm, and then sequentially updated frame by frame. From the GMM, a self-regulatory threshold for discrimination is derived at each subband. Some constraints are introduced to this GMM for the sake of reliability. For the reason of unsupervised learning, the proposed VAD does not rely on an assumption that the first several frames of an utterance are nonspeech, which is widely used in most VADs. Moreover, the speech presence probability in the time-frequency domain is a byproduct of this VAD. We tested it on speech from TIMIT database and noise from NOISEX-92 database. The evaluations effectively showed its promising performance in comparison with VADs such as ITU G.729B, GSM AMR, and a typical semi-supervised VAD.
@cite_15: One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models. In addition to the Gaussian model, we also incorporate the complex Laplacian and Gamma probability density functions to our analysis of statistical properties. With a goodness-of-fit tests, we analyze the statistical properties of the DFT spectra of the noisy speech under various noise conditions. Based on the statistical analysis, the likelihood ratio test under the given statistical models is established for the purpose of VAD. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in both the stationary and nonstationary noise environments is evaluated with the aid of an objective measure.
In respect of the VAD study, the distribution difference between different noise scenarios has been mentioned in traditional VADs. For example, in @cite_15 , Chang used different statistical models for modeling the speech and noise distributions in different noise scenarios. Another related topic with domain adaptation is the online learning methods @cite_5 , they update the model parameters according to the historical domain information of the speech signals. Traditional statistical-model-based VADs @cite_15 can also be regarded as unsupervised online learning methods. But to our knowledge, how to combine multiple features effectively is still an open problem in the online learning methods. On the other side, although the domain-adaptation-based VAD works in batch mode, it can combine multiple features effectively and yield a high accuracy without a requirement of heavy manual labeling.