Speaker Adaptation for Multichannel End-to-End Speech Recognition

Tsubasa Ochiai; Shinji Watanabe; Shigeru Katagiri; Takaaki Hori; John Hershey

doi:10.1109/ICASSP.2018.8462161

Speaker Adaptation for Multichannel End-to-End Speech Recognition

Tsubasa Ochiai, Shinji Watanabe, Shigeru Katagiri, Takaaki Hori, John Hershey

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beamformer, attention mechanism, or decoder network.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6707-6711
Number of pages	5
Volume	2018-April
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8462161
Publication status	Published - 2018 Sep 10
Externally published	Yes
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 2018 Apr 15 → 2018 Apr 20

Other

Other	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country	Canada
City	Calgary
Period	18/4/15 → 18/4/20

Keywords

Attention-based encoder-decoder
Multichannel end-to-end ASR
Neural beamformer
Speaker adaptation

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2018.8462161

Fingerprint Dive into the research topics of 'Speaker Adaptation for Multichannel End-to-End Speech Recognition'. Together they form a unique fingerprint.

Cite this

Ochiai, T., Watanabe, S., Katagiri, S., Hori, T., & Hershey, J. (2018). Speaker Adaptation for Multichannel End-to-End Speech Recognition. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 6707-6711). [8462161] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462161

Speaker Adaptation for Multichannel End-to-End Speech Recognition. / Ochiai, Tsubasa; Watanabe, Shinji; Katagiri, Shigeru; Hori, Takaaki; Hershey, John.

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 6707-6711 8462161.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ochiai, T, Watanabe, S, Katagiri, S, Hori, T & Hershey, J 2018, Speaker Adaptation for Multichannel End-to-End Speech Recognition. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. vol. 2018-April, 8462161, Institute of Electrical and Electronics Engineers Inc., pp. 6707-6711, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 18/4/15. https://doi.org/10.1109/ICASSP.2018.8462161

Ochiai T, Watanabe S, Katagiri S, Hori T, Hershey J. Speaker Adaptation for Multichannel End-to-End Speech Recognition. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Vol. 2018-April. Institute of Electrical and Electronics Engineers Inc. 2018. p. 6707-6711. 8462161 https://doi.org/10.1109/ICASSP.2018.8462161

@inproceedings{ba69d5bded0f46a3a75e6ee9a89f550f,

title = "Speaker Adaptation for Multichannel End-to-End Speech Recognition",

abstract = "Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beamformer, attention mechanism, or decoder network.",

keywords = "Attention-based encoder-decoder, Multichannel end-to-end ASR, Neural beamformer, Speaker adaptation",

author = "Tsubasa Ochiai and Shinji Watanabe and Shigeru Katagiri and Takaaki Hori and John Hershey",

year = "2018",

month = sep,

day = "10",

doi = "10.1109/ICASSP.2018.8462161",

language = "English",

isbn = "9781538646588",

volume = "2018-April",

pages = "6707--6711",

booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

}

TY - GEN

T1 - Speaker Adaptation for Multichannel End-to-End Speech Recognition

AU - Ochiai, Tsubasa

AU - Watanabe, Shinji

AU - Katagiri, Shigeru

AU - Hori, Takaaki

AU - Hershey, John

PY - 2018/9/10

Y1 - 2018/9/10

N2 - Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beamformer, attention mechanism, or decoder network.

AB - Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beamformer, attention mechanism, or decoder network.

KW - Attention-based encoder-decoder

KW - Multichannel end-to-end ASR

KW - Neural beamformer

KW - Speaker adaptation

UR - http://www.scopus.com/inward/record.url?scp=85054252839&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054252839&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2018.8462161

DO - 10.1109/ICASSP.2018.8462161

M3 - Conference contribution

AN - SCOPUS:85054252839

SN - 9781538646588

VL - 2018-April

SP - 6707

EP - 6711

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Y2 - 15 April 2018 through 20 April 2018

ER -