Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition

• Published in: IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2017-05, Vol.25 (5), p.965-979
• Main Authors: Sainath, Tara N., Weiss, Ron J., Wilson, Kevin W., Li, Bo, Narayanan, Arun, Variani, Ehsan, Bacchiani, Michiel, Shafran, Izhak, Senior, Andrew, Chin, Kean, Misra, Ananya, Kim, Chanwoo
• Format: Article
• Language: English
• Subjects: Acoustics; Adaptive filters; Array signal processing; Artificial neural networks; Auditory localization; Automatic speech recognition; Beamforming; Computer architecture; Deep learning; Direction of arrival; Error analysis; Localization; Microphone arrays; Microphones; Modelling; Multichannel communication; Neural networks; noise-robust speech recognition; Signal processing; Spatial filtering; Speech; Speech enhancement; Speech processing; Speech recognition; Time; Training; Voice recognition; Waveforms
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2017.2672401

Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture, which performs multichannel filtering in the first layer of the network, and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally, we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model.