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We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noise-corrupted ("missing") components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed using both sparse imputation and cluster-based GMM imputation. Compared to two reference mask estimation techniques based on interaural level and time difference-pairs, the proposed missing data approach significantly improved the keyword accuracy rates in all signal-to-noise ratio conditions when evaluated on the CHiME reverberant multisource environment corpus. Of the imputation methods, cluster-based imputation was found to outperform sparse imputation. The highest keyword accuracy was achieved when the system was trained on imputed data, which made it more robust to possible imputation errors.

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This paper describes a novel two-stage dereverberation feature enhancement method for noise-robust automatic speech recognition. In the first stage, an estimate of the dereverberated speech is generated by matching the distribution of the observed reverberant speech to that of clean speech, in a decorrelated transformation domain that has a long temporal context in order to address the effects of reverberation. The second stage uses this dereverberated signal as an initial estimate within a non-negative matrix factorization framework, which jointly estimates a sparse representation of the clean speech signal and an estimate of the convolutional distortion. The proposed feature enhancement method, when used in conjunction with automatic speech recognizer back-end processing, is shown to improve the recognition performance compared to three other state-of-the-art techniques.

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