Google Research

Spectral distortion model for training phase-sensitive deep-neural networks for far-field speech recognition

Abstract

In this paper, we present an algorithm which introduces phaseperturbation to the training database when training phase-sensitive deep neural-network models. Traditional features such as log-mel or cepstral features do not have have any phase-relevant information. However more recent features such as raw-waveform or complex spectra features contain phase-relevant information. Phase-sensitive features have the advantage of being able to detect differences in time of arrival across different microphone channels or frequency bands. However, compared to magnitude-based features, phase information is more sensitive to various kinds of distortions such as variations in microphone characteristics, reverberation, and so on. For traditional magnitude-based features, it is widely known that adding noise or reverberation, often called Multistyle-TRaining (MTR) , improves robustness. In a similar spirit, we propose an algorithm which introduces spectral distortion to make the deep-learning model more robust against phase-distortion. We call this approach Spectral-Distortion TRaining (SDTR) and Phase-Distortion TRaining (PDTR). In our experiments using a training set consisting of 22-million utterances, this approach has proved to be quite successful in reducing Word Error Rates in test sets obtained with real microphones on Google Home

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work