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 | |
| 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 |
Fingerprint
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
Cite this
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
}
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
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.
ER -