Beamforming networks using spatial covariance features for far-field speech recognition

Xiong Xiao, Shinji Watanabe, Eng Siong Chng, Haizhou Li

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Recently, a deep beamforming (BF) network was proposed to predict BF weights from phase-carrying features, such as generalized cross correlation (GCC). The BF network is trained jointly with the acoustic model to minimize automatic speech recognition (ASR) cost function. In this paper, we propose to replace GCC with features derived from input signals' spatial covariance matrices (SCM), which contain the phase information of individual frequency bands. Experimental results on the AMI meeting transcription task shows that the BF network using SCM features significantly reduces the word error rate to 44.1% from 47.9% obtained with the conventional ASR pipeline using delay-and-sum BF. Also compared with GCC features, we have observed small but steady gain by 0.6% absolutely. The use of SCM features also facilitate the implementation of more advanced BF methods within a deep learning framework, such as minimum variance distortionless response BF that requires the speech and noise SCM.

本文言語English
ホスト出版物のタイトル2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9789881476821
DOI
出版ステータスPublished - 2017 1 17
外部発表はい
イベント2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
継続期間: 2016 12 132016 12 16

Other

Other2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
CountryKorea, Republic of
CityJeju
Period16/12/1316/12/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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