Enhanced Spectral Features for Spoken Language Identification

摘要：

To date,systems for the identification of spoken languages have normally used spectral features such as the MFCC.It has also been shown that prosody features such as pitch and intensity have an important role to increase the accuracy of LID.In this paper,we used three novel features based on spectrum,in combination with MFCC and prosody features to improve language identification accuracy.These features are spectral centroid,Renyi entropy and shannon entropy.The basic system used is GMM_LM with back-end classifier.Also we used a new method to convert the output of the Language model scores to the new vectors to increase the LID performance.Using three new features and LM score conversion,an improved accuracy of about 8％,in comparison to the baseline system,on five of the languages of OGI_TS multilingual telephone speech corpus,was obtained.

作者: Ali Ziaei Seyed Mohammad Ahadi Hojatollah Yeganeh

作者单位: Speech Processing Research Laboratory Electrical Engineering Department,Amirkabir University of Technology Hafez Avenue,Tehran 15914,Iran

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）