Investigating neural network based query-by-example keyword spotting approach for personalized wake-up word detection in Mandarin Chinese

Jingyong Hou, Lei Xie, Zhonghua Fu

科研成果: 书/报告/会议事项章节会议稿件同行评审

18 引用 (Scopus)

摘要

We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In this paper, we use neural networks as acoustic models to extract DNN/LSTM phoneme posterior features and LSTM embedding features. Specifically, we investigate the LSTM embedding feature extractor for different modeling units in Mandarin, spanning from phonemes to words. We also study the performances of two popular DTW approaches: S-DTW and SLN-DTW. SLN-DTW manages to accurately and effectively search the keyword in a long audio stream without the segmentation procedure that is used in S-DTW approaches. Our study shows that DNN phoneme posterior plus SLN-DTW approach achieves the highest computation efficiency and the state-of-the-art performance with 78% relative miss rate reduction as compared with the S-DTW approach. Word level LSTM embedding feature shows superior performance as compared with other embedding units.

源语言英语
主期刊名Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
编辑Hsin-Min Wang, Qingzhi Hou, Yuan Wei, Tan Lee, Jianguo Wei, Lei Xie, Hui Feng, Jianwu Dang, Jianwu Dang
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781509042937
DOI
出版状态已出版 - 2 5月 2017
活动10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, 中国
期限: 17 10月 201620 10月 2016

出版系列

姓名Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016

会议

会议10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
国家/地区中国
Tianjin
时期17/10/1620/10/16

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