Reverberation robust speech recognition by matching distributions of spectrally and temporally decorrelated features

This paper addresses dereverberation of speech using an unsupervised approach utilizing speech prior and taking only weak assumptions on reverberation. Our approach uses a long time context representation of reverberated speech in spectral-temporal supervectors which are decorrelated by the PCA. In the decorrelated domain supervectors are mapped from reverberant speech distribution to clean speech distribution and then to mel-spectral vectors. Mel-domain Wiener filter is applied as post processing. Our results demonstrate performance gains over the provided baseline recognizer, and show that the method can be coupled to CMLLR adaptation with cumulative benefits for clean trained models. Furthermore, we show that using dimensionality reduction coupled with the Wiener filter is better than using full dimensional PCA in representing small variance components in speech.