基于DTW的语音关键词检出

侯靖勇, 谢磊, 杨鹏, 肖雄, 梁祥智, 徐海华, 王磊, 吕航, 马斌, CHNG EngSiong, 李海洲

清华大学学报（自然科学版） ›› 2017, Vol. 57 ›› Issue (1) : 18-23.

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清华大学学报（自然科学版） ›› 2017, Vol. 57 ›› Issue (1) : 18-23. DOI: 10.16511/j.cnki.qhdxxb.2017.21.004
计算机科学与技术

基于DTW的语音关键词检出

  • 侯靖勇1, 谢磊1, 杨鹏1, 肖雄2, 梁祥智3, 徐海华2, 王磊3, 吕航1, 马斌3, CHNG EngSiong2,4, 李海洲2,3,4
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Spoken term detection based on DTW

  • HOU Jingyong1, XIE Lei1, YANG Peng1, XIAO Xiong2, LEUNG Cheung-Chi3, XU Haihua2, WANG Lei3, LV Hang1, MA Bin3, CHNG EngSiong2,4, LI Haizhou2,3,4
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摘要

针对少资源语言的语音关键词检出技术受到了广泛关注。该文在基于动态时间规整（dynamic time warping，DTW）的关键词检出框架下，提出了基于音素边界的局部匹配策略，用以解决基于样例的语音关键词检出任务中的近似查询问题。在QUESST 2014评测数据上采用多种特征进行了实验验证。实验结果显示：基于音素边界的局部匹配策略不仅在近似查询T2和T3任务上的检出效果明显提升，在精确查询T1任务上也获得了有效提升。随后的系统融合实验表明，该策略能够大幅提升融合系统的性能。

Abstract

Spoken term detection (STD) for low resource languages has drawn much interest. A partial matching strategy based on phoneme boundaries is presented here to solve the fuzzy matching problem in query-by-example spoken term detection with dynamic time warping. A variety of features were used to validate the strategy on the QUESST 2014 dataset. Tests show that this strategy is not only quite effective for fuzzy match tasks T2 and T3 but also effective for the exact match task T1. This strategy has significantly improved performance in fusion tests.

关键词

语音关键词检出 / 少资源语言 / 动态时间规整 / 局部匹配

Key words

spoken term detection / low resource languages / dynamic time warping / partial matching

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侯靖勇, 谢磊, 杨鹏, 肖雄, 梁祥智, 徐海华, 王磊, 吕航, 马斌, CHNG EngSiong, 李海洲. 基于DTW的语音关键词检出[J]. 清华大学学报（自然科学版）. 2017, 57(1): 18-23 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.004
HOU Jingyong, XIE Lei, YANG Peng, XIAO Xiong, LEUNG Cheung-Chi, XU Haihua, WANG Lei, LV Hang, MA Bin, CHNG EngSiong, LI Haizhou. Spoken term detection based on DTW[J]. Journal of Tsinghua University(Science and Technology). 2017, 57(1): 18-23 https://doi.org/10.16511/j.cnki.qhdxxb.2017.21.004
中图分类号： TP391.3   

参考文献

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