Recognition of reverberant speech by missing data imputation and NMF feature enhancement

Abstract

The problem of reverberation in speech recognition is addressed in this study by extending a noise-robust feature enhancement method based on non-negative matrix factorization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a melspectral feature domain convolution to account for the effects of room reverberation. The proposed method is contrasted with missing data techniques for reverberant speech, and evaluated for speech recognition performance using the REVERB challenge corpus. Our results indicate consistent gains in recognition performance compared to the baseline system, with a relative improvement in word error rate of 42.6% for the optimal case.

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@inproceedings{Kallasjoki2014RecognitionOR, title={Recognition of reverberant speech by missing data imputation and NMF feature enhancement}, author={Heikki Kallasjoki and Jort F. Gemmeke and K. Palomaki and Amy V. Beeston and Guy J. Brown and Kalle J. Palom{\"a}ki}, year={2014} }