Paper Info
Title
DIRECTIONAL ASR: A NEW PARADIGM FOR E2E MULTI-SPEAKER SPEECH RECOGNITION WITH SOURCE LOCALIZATION
Abstract
This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end (E2E) neural network manner, called directional automatic speech recognition (D-ASR), which explicitly models source speaker locations. In D-ASR, the azimuth angle of the sources with respect to the microphone array is defined as a latent variable. This angle controls the quality of separation, which in turn determines the ASR performance. All three functionalities of D-ASR: localization, separation, and recognition are connected as a single differentiable neural network and trained solely based on ASR error minimization objectives. The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined. In addition, D-ASR does not require explicit direction of arrival (DOA) supervision like existing data-driven localization models, which makes it more appropriate for realistic data. For the case of two source mixtures, D-ASR achieves an average DOA prediction error of less than three degrees. It also outperforms a strong far-field multi-speaker end-to-end system in both separation quality and ASR performance.
Year
DOI
Venue
2021
10.1109/ICASSP39728.2021.9414243
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)
Keywords
DocType
Citations 
source localization, source separation, end-to-end speech recognition
Conference
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
S. Aswin Shanmugam174.21
Chao Weng211319.75
Shinji Watanabe31158139.38
Meng Yu452466.52
Yong Xu527624.71
Shi-Xiong Zhang6186.75
Dong Yu76264475.73