基于谱熵梅尔积的语音端点检测方法

doi:10.13190/j.jbupt.2018-228

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (2): 83-89.doi: 10.13190/j.jbupt.2018-228

基于谱熵梅尔积的语音端点检测方法

吴新忠, 夏令祥, 张旭, 周成

中国矿业大学信息与控制工程学院, 江苏徐州 221116

收稿日期:2018-09-11 出版日期:2019-04-28 发布日期:2019-04-09
作者简介:吴新忠(1976-),男,副教授,硕士生导师,E-mail:wxzcumt@126.com.
基金资助:
"十三五"国家重点研发计划项目（2016YFC0801800）；江苏省重点研发计划项目（BE2016046）

Voice Activity Detection Method Based on MFPH

WU Xin-zhong, XIA Ling-xiang, ZHANG Xu, ZHOU Cheng

School of Information and Control Engineering, China University of Mining and Technology, Jiangsu Xuzhou 221116, China

Received:2018-09-11 Online:2019-04-28 Published:2019-04-09

摘要/Abstract

摘要： 为了克服传统语音端点检测算法在低信噪比环境下准确率低的问题，提出一种基于谱熵梅尔积（MFPH）的语音端点检测算法.首先，提取带噪语音信号的梅尔频率倒谱系数中的第一维参数MFCC₀，将其与谱熵的乘积作为最终区分语音段和背景噪声段的融合特征参数；然后，结合模糊C均值聚类算法和贝叶斯信息准则（BIC）算法对MFPH特征参数门限值进行自适应估计；最后，采用双门限法进行语音端点检测.实验结果证明，与传统方法比较，该方法在-5~15 dB低信噪比环境下的语音端点检测准确率有较大提高.

关键词: 语音端点检测, 梅尔频率倒谱系数, 谱熵, 谱熵梅尔积, 双门限法, 低信噪比

Abstract: In order to solve the problem that the accuracy of traditional voice activity detection algorithms is low in the low signal-to-noise ratio (SNR) environment,a voice activity detection algorithm based on product of spectral entropy and Mel (MFPH) was proposed. Firstly, the first dimensional parameter MFCC₀ of Mel frequency spectrum coefficient of the speech signal with noisy was extracted, and the product of MFCC₀ and spectral entropy was taken as fusion characteristic parameter of finally distinguishing speech segment from background noise. Then, the threshold value of MFPH characteristic parameters was estimated adaptively based on combination of fuzzy C-means clustering algorithm (FCM) and Bayesian information criterion (BIC). Finally, the double-threshold method was adopted for the voice activity detection. Experiments show that the accuracy of the proposed method is greatly improved in the -5~15 dB low SNR environment compared with traditional methods.

Key words: voice activity detection, Mel frequency spectrum coefficient, spectral entropy, spectral entropy Mel product, double-threshold method, low signal-to-noise ratio

中图分类号:

TN912.3

吴新忠, 夏令祥, 张旭, 周成. 基于谱熵梅尔积的语音端点检测方法[J]. 北京邮电大学学报, 2019, 42(2): 83-89.

WU Xin-zhong, XIA Ling-xiang, ZHANG Xu, ZHOU Cheng. Voice Activity Detection Method Based on MFPH[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(2): 83-89.

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基于谱熵梅尔积的语音端点检测方法

Voice Activity Detection Method Based on MFPH

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