FAR-FIELD SPEECH RECOGNITION USING CNN-DNN-HMM WITH CONVOLUTION IN TIME

Recent studies in speech recognition have shown that the performance of convolutional neural networks (CNNs) is superior to that of fully connected deep neural networks (DNNs). In this paper, we explore the use of CNNs in far-field speech recognition for dealing with reverberation, which blurs spectral energies along the time axis. Unlike most previous CNN applications to speech recognition, we consider convolution in time to examine whether it provides an improved reverberation modelling capability. Experimental results show that a CNN coupled with a fully connected DNN can model short time correlations in feature vectors with fewer parameters than a DNN and thus generalise better to unseen test environments. Combining this approach with signal-space dereverberation, which copes with long-term correlations, is shown to result in further improvement, where the gains from both approaches are almost additive. An initial investigation of the use of restricted convolution forms is also undertaken.