Domain adversarial training for accented speech recognition

Sining Sun, Ching Feng Yeh, Mei Yuh Hwang, Mari Ostendorf, Lei Xie

科研成果: 书/报告/会议事项章节会议稿件同行评审

95 引用 (Scopus)

摘要

In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data ('standard' accent) and unlabeled target domain data (with heavy accents), we augment the learning objective for a Kaldi TDNN network with a domain adversarial training (DAT) objective to encourage the model to learn accent-invariant features. In experiments with three Mandarin accents, we show that DAT yields up to 7.45% relative character error rate reduction when we do not have transcriptions of the accented speech, compared with the baseline trained on standard accent data only. We also find a benefit from DAT when used in combination with training from automatic transcriptions on the accented data. Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition.

源语言英语
主期刊名2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
4854-4858
页数5
ISBN(印刷版)9781538646588
DOI
出版状态已出版 - 10 9月 2018
活动2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, 加拿大
期限: 15 4月 201820 4月 2018

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2018-April
ISSN(印刷版)1520-6149

会议

会议2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
国家/地区加拿大
Calgary
时期15/04/1820/04/18

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