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
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
Research Areas
