[1]解焱陆,张劲松,刘明辉,等.基于分层增长语音活动检测的鲁棒性说话人识别[J].深圳大学学报理工版,2012,29(No.4(283-376)):328-334.[doi:10.3724/SP.J.1249.2012.04328]
　XIE Yan-lu,ZHANG Jing-song,LIU Ming-hui,et al.Robust speaker recognition based on level-building voice activity detection[J].Journal of Shenzhen University Science and Engineering,2012,29(No.4(283-376)):328-334.[doi:10.3724/SP.J.1249.2012.04328]
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基于分层增长语音活动检测的鲁棒性说话人识别()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第29卷
期数:
2012年No.4(283-376)
页码:
328-334
栏目:
电子与信息科学
出版日期:
2012-07-25

文章信息/Info

Title:
Robust speaker recognition based on level-building voice activity detection
文章编号:
20120409
作者:
解焱陆1;  张劲松1; 刘明辉2; 黄中伟2
1) 北京语言大学信息科学学院，北京 100083；2) 深圳大学语音实验室，深圳 518060
Author(s):
XIE Yan-lu1;  ZHANG Jing-song1;  LIU Ming-hui2;  and HUANG Zhong-wei2
1) College of Information Science, Beijing Language and Culture University, Beijing 100083, P.R.China
2) Phonetic Laboratory, Shenzhen University, Shenzhen 518060, P.R.China
关键词:
语音信号处理; 说话人识别; 分布式语音识别; 分层增长; 语音活动检测; 似然距离
Keywords:
speech signal processing;  speaker identification;  distributed speech recognition;  level-building;  voice activity detection;  likelihood measurement
分类号:
TN 912.34; TP 391.4
DOI:
10.3724/SP.J.1249.2012.04328
文献标志码:
A
摘要:
基于欧洲电信标准化协会颁布的分布式语音识别和前端标准(ETSI-DSR-AFE)．针对分布式说话人识别噪声鲁棒性较差的问题，提出一种新的前端处理方法．该方法以似然距离为测度，对语音进行无监督聚类，为减少计算量，采用分层增长（level-building）方法进行逐层分割，从而准确找出语音和静音的边界点．实验结果表明，用该方法改进ETSI-DSR-AFE 标准后，信噪比在大于0 dB时，说话人辨认系统识别率相对改进了18.9%，相对原有的Mel频率倒谱系数（Mel-frequenly Ceptral coefficients，MFCC）系统识别率改进了60.7%．
Abstract:
A level-building and two-stage Wiener filter methodology is proposed to improve the robustness in distributed noise speech recognition in ETSI（European Telecommunications Standards Institute)-DSR（Distributed Speech Recognition)-AFE（Advanced Front-End）standard. The speech is clustered in an unsupervised with a likelihood measurement．The level-building process for dividing speech at each level is introduced to reduce the computational load. Therefore, the boundaries of voice and non-voice data are precisely detected．Experiments have demonstrated that performance of this proposed methodology shows improvement by 18.9% in ETSI-DSR-AFE standard when the SNR of speech is greater than 0 dB．The recognition rate is also improved by 60.7% in comparison with that of Mel-frequenly Ceptral coefficients（MFCC） system．

参考文献/References:

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备注/Memo

备注/Memo:
基金项目：国家自然科学基金项目（61005020）；中央高校基本科研业务费专项资金资助项目（10JBT01）
作者简介：解焱陆 (1980-)，男（汉族），江苏省扬州市人，北京语言大学副教授、博士. E-mail: xieyanlu@blcu.edu.cn
引文：解焱陆, 张劲松,刘明辉，等．基于分层增长语音活动检测的鲁棒性说话人识别［J］. 深圳大学学报理工版，2012，29（4）：328-334.
Received：2011-12-12；Revised：2012-05-08；Accepted：2012-06-04
Foundation：National Natural Science Foundation of China(61005020)；Fundamental Research Funds for the Central University (10JBT01)
Corresponding author：Associate professor XIE Yan-lu. E-mail: xieyanlu@blcu.edu.cn
Citation：XIE Yan-lu，ZHANG Jing-song，LIU Ming-hui，et al．Robust speaker recognition based on level-building voice activity detection［J］. Journal of Shenzhen University Science and Engineering, 2012, 29(4): 328-334.（in Chinese）
更新日期/Last Update: 2012-07-29