Application of noise robust MDT speech recognition on the SPEECON and speechdat-car databases

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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Improving robustness of deep neural networks via spectral masking for automatic speech recognition

Sim

2013

2013 IEEE Workshop on Automatic Speech Recognition and Understanding

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Application of noise robust MDT speech recognition on the SPEECON and speechdat-car databases

Cited by 6 publications

References 14 publications

Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data

Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data

Mask estimation and imputation methods for missing data speech recognition in a multisource reverberant environment

Improving robustness of deep neural networks via spectral masking for automatic speech recognition

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