说话人识别中的分数域语速归一化

艾斯卡尔·肉孜, 王东, 李蓝天, 郑方, 张晓东, 金磐石

清华大学学报（自然科学版） ›› 2018, Vol. 58 ›› Issue (4) : 337-341.

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清华大学学报（自然科学版） ›› 2018, Vol. 58 ›› Issue (4) : 337-341. DOI: 10.16511/j.cnki.qhdxxb.2018.25.028
计算机科学与技术

说话人识别中的分数域语速归一化

  • 艾斯卡尔·肉孜1, 王东1, 李蓝天1, 郑方1, 张晓东2, 金磐石2
作者信息 +

Score domain speaking rate normalization for speaker recognition

  • AISIKAER Rouzi1, WANG Dong1, LI Lantian1, ZHENG Fang1, ZHANG Xiaodong2, JIN Panshi2
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摘要

语速变化导致说话人识别系统性能显著下降。该文提出一种分数域语速归一化方法来降低语速变化对说话人识别系统的影响。由不同语速语音数据组成参考集（全局和局部），对每一个登入说话人估计其对参考集中每一类参考语音的分数分布，局部参考集通过按相对语速划分全局参考集而获得。基于该文录制的语速数据库在GMM-UBM（Gaussian mixture model-universal background model）框架下对测试语音进行分数归一化，并通过训练数据扩展有效解决了数据系数问题，最终等错误率相对下降33.33%。研究结果表明：全局和局部归一化方法都有效减少了语速变化对说话人识别系统的影响。

Abstract

Speaking rate variations seriously degrade speaker recognition accuracy. This paper presents a normalization approach in the score domain that reduces the impact of speaking rate variations. The score distributions for each type of imposter in the cohort set (global and local sets which consist of speech utterances at different speaking rates) are computed against each enrolled speaker with the local cohort set obtained by splitting the utterances in the global cohort set according to the relative speaking rates. The scores for the test speech are normalized based on a self-recorded speaking rate database using a GMM-UBM (Gaussian mixture model-universal background model) framework with the data sparsity problem handled by augmenting the training data with a final relative EER (equal error rate) reduction of 33.33%. This study shows that global and local score normalization methods effectively reduce the impact of speaking rate variations on speaker recognition.

关键词

说话人识别 / 分数域 / 语速归一化 / 相对语速 / GMM-UBM

Key words

speaker recognition / score domain / speaking rate normalization / relative speaking rate / GMM-UBM

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艾斯卡尔·肉孜, 王东, 李蓝天, 郑方, 张晓东, 金磐石. 说话人识别中的分数域语速归一化[J]. 清华大学学报（自然科学版）. 2018, 58(4): 337-341 https://doi.org/10.16511/j.cnki.qhdxxb.2018.25.028
AISIKAER Rouzi, WANG Dong, LI Lantian, ZHENG Fang, ZHANG Xiaodong, JIN Panshi. Score domain speaking rate normalization for speaker recognition[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(4): 337-341 https://doi.org/10.16511/j.cnki.qhdxxb.2018.25.028
中图分类号： TP391.4   

参考文献

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[2] BIMBOT F, BONASTRE J F, FREDOUILLE C. A tutorial on text-independent speaker verification[J]. EURASIP Journal on Applied Signal Processing, 2004(1):430-451.
[3] CHU M S, POVEY D. Speaking rate adaptation using continuous frame rate normalization[C]//Proceedings of the International Conference on Acoustics, Speech and Signal Processing. Dallas, TX, USA:IEEE, 2010:4306-4309.
[4] XU M X, ZHANG L P, WANG L L. Database collection for study on speech variation robust speaker recognition[C]//Proceedings of the Oriental Chapter of the International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques. Kyoto, Japan:IEEE, 2008.
[5] MARCO G, CUMMINS F. Speech style and speaker recognition:A case study[C]//Proceedings of the Interspeech. Brighton, UK:IEEE, 2009.
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[7] VAN HEERDEN C J, BARNARD E. Speech rate normalization used to improve speaker verification[J]. SAIEE Africa Research Journal, 2007, 98(4):129-135.
[8] BEIGI H. Fundamentals of speaker recognition[M]. New York, USA:Springer, 2011.
[9] MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9:2579-2605.
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[11] CUMMINS F, GRIMALDI M, LEONARD T, et al. The chains corpus:Characterizing individual speakers[C]//Proceedings of the International Conference on Speech and Computer (SPECOM), St. Petersburg, Russia:Springer, 2006:431-435.
[12] POVEY D, GHOSHAL A, BOULIANNE G, et al. The KALDI speech recognition toolkit[C]//Proceedings of the Automatic Speech Recognition and Understanding (ASRU). Hawaii, HI, USA:IEEE, 2011. USA:IEEE, 2010:4306-4309.
[4] XU M X, ZHANG L P, WANG L L. Database collection for study on speech variation robust speaker recognition[C]//Proceedings of the Oriental Chapter of the International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques. Kyoto, Japan:IEEE, 2008.
[5] MARCO G, CUMMINS F. Speech style and speaker recognition:A case study[C]//Proceedings of the Interspeech. Brighton, UK:IEEE, 2009.
[6] ASKAR R, LI L T, WANG D, et al. Feature transformation for speaker verification under speaking rate mismatch condition[C]//Proceedings of the Asia-Pacific Signal and Information Processing Association. Jeju, Korea:IEEE, 2016.
[7] VAN HEERDEN C J, BARNARD E. Speech rate normalization used to improve speaker verification[J]. SAIEE Africa Research Journal, 2007, 98(4):129-135.
[8] BEIGI H. Fundamentals of speaker recognition[M]. New York, USA:Springer, 2011.
[9] MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9:2579-2605.
[10] van der MAATEN L, HINTON G. Visualizing non-metric similarities in multiple maps[J]. Machine Learning, 2012, 87(1):33-55.
[11] CUMMINS F, GRIMALDI M, LEONARD T, et al. The chains corpus:Characterizing individual speakers[C]//Proceedings of the International Conference on Speech and Computer (SPECOM), St. Petersburg, Russia:Springer, 2006:431-435.
[12] POVEY D, GHOSHAL A, BOULIANNE G, et al. The KALDI speech recognition toolkit[C]//Proceedings of the Automatic Speech Recognition and Understanding (ASRU). Hawaii, HI, USA:IEEE, 2011.

基金

国家自然科学基金资助项目（61271389，61371136）；国家“九七三”重点基础研究发展计划（2013CB329302）

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