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基于GMM多维概率输出的SVM话者确认 被引量：2

Speaker Verification Based on GMM Multidimensional Likelihoods and SVM
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摘要 提出一种结合统计模型与区分性模型优点的说话人确认方法:基于GMM多维概率输出的SVM话者模型的说话人确认.以目标说话人的GMM模型对一条语音的不同特征分量的概率输出作为特征参数,建立目标说话人的SVM模型.在NIST’05 8conv4w-lconv4w数据库上的实验表明该方法的有效性. In this paper, a text-independent speaker verification system based on GMM multidimensional likelihoods and SVM is proposed, which combines the advantages of both generative model and discriminative model. In this method, the GMM multidimensional likelihoods for the test speech are regarded as new features for SVM. Experiment results of text-independent speaker verification on NIST＇05 8conv4w-1conv4w database show effectiveness of the proposed system.
作者 刘明辉 戴蓓蒨 解焱陆
机构地区 中国科学技术大学多媒体计算与通信教育部-微软重点实验室 中国科学技术大学电子科学与技术系
出处 《模式识别与人工智能》 EI CSCD 北大核心 2008年第1期28-33,共6页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金资助项目(No.60272039)
关键词 说话人确认 GMM多维概率输出 支持向量机(SVM) 文本无关 Speaker Verification, Gaussian Mixture Model Multidimensional Likelihoods,Support Vector Machine （SVM）, Text-Independent
分类号 TN912.34 [电子电信—通信与信息系统]
  • 相关文献

参考文献12

  • 1Vapnik V N. The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
  • 2Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167.
  • 3黄伟,戴蓓蒨.基于GMM统计特性参数和SVM的话者确认[J].数据采集与处理,2004,19(4):365-370. 被引量：5
  • 4Wan V, Campbell W M. Support Vector Machines for Speaker Verification and Identification Proc of the International Workshop of Neural Networks for Signal Processing. Sydney, Australia, 2000, X: 775-784.
  • 5Wan V, Renals S. SVMSVM: Support Vector Machine Speaker Verification Methodology Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Hong Kong, China, 2003, Ⅱ : 221-224.
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二级参考文献6

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共引文献4

同被引文献17

  • 1黄伟,戴蓓蒨.基于GMM统计特性参数和SVM的话者确认[J].数据采集与处理,2004,19(4):365-370. 被引量：5
  • 2Campbell W M, Sturim D E, Reynolds D A. Support Vector Machines Using GMM Supervectors for Speaker Verification[J]. IEEE Signal Processing Letters, 2006, 13(5): 308-311.
  • 3Liu Minghui, Xie Yanlu, Yao Zhiqiang, et al. A New Hybrid GMM/SVM for Speaker Verification[C]//Proc. of IEEE ICPR'06. Hong Kong, China: [s. n.], 2006.
  • 4Sturim D E, Reynolds E A. Speaker Adaptive Cohort Selection for Tnorm in Text-independent Speaker Verification[C]//Proc. of ICASSP'05. Philadelphia, USA: [s. n.], 2005.
  • 5Przybocki M A, Martin A E Le A N. NIST Speaker Recognition Evaluations Utilizing the Mixer Corpora 2004, 2005, 2006[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(7): 1951-1959.
  • 6Wan V, Renals S. Speaker Verification Using Sequence Discriminant Support Vector Machines[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2005, 13(2): 203-210.
  • 7Louradour J, Daoudi K, Bach E Feature Space Mahalanobis Sequence Kernels: Application to SVM Speaker Verification[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(8): 2465-2475.
  • 8Reynolds D A. Speaker Verification Using Adapted Gaussian Mixture Models[J]. Digital Signal Processing, 2000, 10(1): 19-41.
  • 9LAI Y X, LAI C F, HUANG Y M, et al. Multi-appliance rec- ognition system with hybrid SVM/GMM classifier in ubiqui- tous smart home [J]. Information Sciences, 2013,230(1):39- 55.
  • 10WU Dalei, LI Ji, WU Haiqing. a-gaussian mixture modelling for speaker recognition [J]. Pattern Recognition Letters, 2009, 30(6) :589-594.

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模式识别与人工智能
2008年 第1期

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