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基于多项式拟合的中性-情感模型转换算法 被引量：1

Neutral-emotion model transformation algorithm based on polynomial function fitting.
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摘要 情绪变化问题是说话人识别技术面临的一个难题。为了解决该问题,提出了基于多项式方程拟合的中性-情感模型转换算法。该算法建立了中性模型和情感模型之间的函数关系,只需要说话人的中性语音就能训练其各种情感类型的说话人模型。在普通话情感语音库上的实验表明,采用该方法后识别算法的等错误率由16.06%降低到10.31%,提高了系统性能。 One of the largest challenges in speaker recognition is dealing with speaker-emotion variability problem.A neutral-emotion model transformation algorithm is presented to overcome this limitation,which builds a relationship between emotion and neutral models.In this method,only neutral speech is needed in training emotion models.The experiments on MASC show that the EER is reduced to 10.31% from the 16.06%,and the recognition performance can be improved by this algorithm.
作者 单振宇 杨莹春
机构地区 浙江大学计算机科学与技术学院
出处 《计算机工程与应用》 CSCD 北大核心 2008年第21期206-208,221,共4页 Computer Engineering and Applications
基金 国家高技术研究发展计划( 863)( the National High-Tech Research and Development Plan of China under Grant No.2006AA01Z136) 浙江省自然科学基金(the Natural Science Foundation of Zhejiang Province of China under Grant No.Y106705)
关键词 说话人识别 高斯混合模型 情感语音 speaker recognition gaussian mixture model emotion speech
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献10

  • 1Scherer K,Johnstone T,Klasmeyer G.Can automatic speaker verification be improved by training the algorithms on emotional speech[C]// Proceedings of ICSLP2000,Beijing, China,2000.IEEE,2000,2:807-810.
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同被引文献14

  • 1李爱军,邵鹏飞,党建武.情感表达的跨文化多模态感知研究[J].清华大学学报（自然科学版）,2009(S1):1393-1401. 被引量：6
  • 2GHIURCAU M V, RUSU C, ASTOLA J. A study of the effect of emotional state upon text-independent speaker identification [C]// International Conference on Acoustics, Speech and Signal Processing. Prague: IEEE, 2011:4944 - 4947.
  • 3BAO H, XU M, ZHENG T F. Emotion attribute projec- tion {or speaker recognition on emotional speech [C]// 8th Annual Conference of the International Speech Communica- tion Association. Antwerp: IEEE, 2007: 758-761.
  • 4HUANG T, YANG Y. Applying pitch-dependent difference detection and modification to emotional speaker recognition [C] // 9th Annual Conference of the International Speech Communication Association. Brisbane: IEEE, 2008:2751-2754.
  • 5HUANG T, YANG Y. Learning virtual HD model for bi-model emotional speaker recognition [C]// International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 1614-1617.
  • 6SHAN Z, YANG Y. Natural-emotion GMM transformation algorithm for emotional speaker recognition [C] // 8th Annual Conference of the International Speech Communication Association. Antwerp: IEEE, 2007:782 - 785.
  • 7SHAN Z, YANG Y. Learning polynomial function based neutral-emotion GMM transformation for emotional speaker recognition [C]// International Conference on Pattern Reeognition. Tampa: IEEE, 2008: 8-11.
  • 8REYNOLDS D A, ROSE R C. Robust text-independent speaker identification using Gaussian mixture speaker models [J]. IEEE Transactions on Speech and Audio Processing, 1995, 3(1): 72- 83.
  • 9REYNOLDS D A, QUATIERI T F, DUNN Q B. Speaker verification using adapted Gaussian mixture models [J]. Digital Signal Processing, 2000, 10(1/2/3) : 19 -41.
  • 10HERSHEY J R, OLSEN P A. Approximating the Kullback Leibler divergence between Gaussian mixture models [C] //International Conference on Acoustics, Speech, and Signal Processing. Honolulu: IEEE, 2007:317 - 320.

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计算机工程与应用
2008年 第21期

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