Enhancement of adaptive de-correlation filtering separation model for robust speech recognition

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on September 25, 2007)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2007.The development of automatic speech recognition (ASR) technology has enabled an increasing number of applications. However, the robustness of ASR under real acoustic environments still remains to be a challenge for practical applications. Interfering speech and background noise have severe degrading effects on ASR. Speech source separation separates target speech from interfering speech but its performance is affected by adverse environmental conditions of acoustical reverberation and background noise. This dissertation works on the enhancement of a speech source separation technique, namely adaptive decorrelation filtering (ADF), for robust ASR applications. To overcome these difficulties and develop practical ADF speech separation algorithms for robust ASR, improvements are introduced in several aspects. From the perspectives of speech spectral characteristics, prewhitening procedures are applied to flatten the long-term speech spectrum to improve adaptation robustness and decrease ADF estimation error. To speedup convergence rate, block-iterative implementation and variable step-size (VSS) methods are proposed. To exploit scenarios where multiple pairs of sensors are available, multi-ADF postprocessing is developed. To overcome the limitations of ADF separation model under background noise, procedures of noise-compensation (NC) and adaptive speech enhancement are proposed for the achievement of improved robustness in diffuse noise. Speech separation simulations and speech recognition experiments are carried out based on TIMIT database and ATR acoustic measurement database. Evaluations of the methods presented in this dissertation demonstrate significant improvement of performances over baseline ADF algorithm in speech separation and recognition.Includes bibliographical reference

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches

Noise adaptive training for subspace Gaussian mixture models

Noise adaptive training (NAT) is an effective approach to normalise the environmental distortions in the training data. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles the problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training. Index Terms: adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture model