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基于GMM区分性训练方法的语言辨识系统 被引量：4

Discriminative Training of GMM for Language Identification
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摘要 文章给出了一种新的语言辨识系统,该系统基于高斯混合模型的区分性训练算法。该区分训练算法在估计模型参数时,采用了广义概率下降法(GPD)和最小分类误差准则(MCE)。利用OGI多语言电话语料库对算法进行了测试,实验表明,该算法是进行语言辨识的一种有效方法。 In this paper,a novel discriminative training procedure for a Gaussian Mixture Model(GMM)language iden-tification system is described.The proposal is based on the Generalized probabilistic Descent (GPD)algorithm and Mini-mum Classification Error Rates formulated to estimate the GMM parameters.The evaluation is conducted using the OGI multi-language telephone speech corpus.The experimental results show such system is very effective in language identifi-cation tasks.
作者 屈丹 王炳锡 藏传辉
机构地区 解放军信息工程大学
出处 《计算机工程与应用》 CSCD 北大核心 2004年第6期108-110,共3页 Computer Engineering and Applications
基金 国家自然科学基金资助课题(编号:60372038)
关键词 高斯混合模型 广义概率下降法 误分类测度 Gaussian Mixture Model(GMM),Generalized Probabilistic Descent (GPD),Misclassification measure
分类号 TN912.3 [电子电信—通信与信息系统]
  • 相关文献

参考文献6

  • 1[1]Y K Muthusamy,E Barnard,R A Cole. Reviewing Automatic Language Identification[J].IEEE Signal Processing Magazine,1994-10
  • 2[2]M A Zissman. Comparison of four approaches to automatic language identification of telephone speech[J].IEEE Trans Speech Audio Processing, 1996 ;4: 31～44
  • 3[3]D A Reynolds,R C Rose. Rosust text-independence speaker identification using Gaussian mixture speaker models[J].IEEE Trans Speech Audio Processing, 1995 ;3( 1 ) :72～83
  • 4[4]W H Tsai,W W Chang. Discriminative training of Gaussian mixture bigram models with applications to Chinese dialect identification[J].Speech Communication, 2002; 36: 317～326
  • 5[5]B H Juang,W Chou,C H Lee. Minimum classification error rate methods for speech recognition[J].IEEE Trans Speech Audio Processing,1997; 5: 257～265
  • 6[6]Y K Muthusamy,R A Cole,B T Oshika. The OGI Multi-language telephone speech corpus[R].Technical report,Center for Spoken Language Understanding Oregon Graduate Institute of Science and Technology, Portland, 1993

同被引文献18

  • 1Zissman M A.Comparison of four approaches to automatic language identification of telephone speech[J].IEEE Transactions on Speech and Audio Processing.1996,4(1):31-44.
  • 2Torres-Carrasquillo P A,Reynolds D A,Deller J R.Language identification Gaussian mixture model tokenization[C]//IEEE Int'l Conf Acoustics,Speech,and Signal Processing,Orlando,May 2002.
  • 3Torres-Carrasquillo P A.Language identification using Gaussian Mixture Models[D].Michigan State University,2002.
  • 4Tsai Wuei-he,Chang Wen-whei.Discrimination training of Guassian mixture bigram models with application to chinese dialect identification[J].Speech Communication,2002.
  • 5Singer E,Torres-Carrasquillo P A,Gleason,et al.Acoustic,phonetic,and discriminative approaches to automatic language recognition[C]//Proc Eurospeech in Geneva,Switzerland,ISCA1-4 September 2003:1345-1348.
  • 6Aluin F M,Przybocki M A,NIST 2003 language recognition evaluation[C]//Proc Eurospeech'03,Septenber 2003:1341-1344.
  • 7Matjěka P,Cernocky J,Sigmund M.Introduction to automatic language identification[C]//Proceedings of Conference Ronadioelektronika 2004,Brno,CZ,STUBA,2004:4.
  • 8Cristianini N,Shawe-taylor J.Support vector machines[M].Cambridge:Cambridge University Press,2000.
  • 9Moreno P,Ho P,A new SVM approach to speaker identification and verification using probabilistic distance kernels[C]//Proc Eurospeech,2003.
  • 10Konodor R,Jebara T,A kernel between sets of vectors[C]//Proc ICML,2003.

引证文献4

二级引证文献9

计算机工程与应用
2004年 第6期

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