Deep Speech Enhancement For Reverberated And Noisy Signals Using Wide Residual Networks

Abstract

This paper proposes a deep speech enhancement method which exploits the high potential of residual connections in a wide neural network architecture, a topology known as Wide Residual Network. This is supported on single dimensional convolutions computed alongside the time domain, which is a powerful approach to process contextually correlated representations through the temporal domain, such as speech feature sequences. We find the residual mechanism extremely useful for the enhancement task since the signal always has a linear shortcut and the non-linear path enhances it in several steps by adding or subtracting corrections. The enhancement capacity of the proposal is assessed by objective quality metrics and the performance of a speech recognition system. This was evaluated in the framework of the REVERB Challenge dataset, including simulated and real samples of reverberated and noisy speech signals. Results showed that enhanced speech from the proposed method succeeded for both, th

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