基于混合GMM和FFBNN的语音情感和说话人自动识别

Q4 Computer Science

International Journal of Computer Science and Applications Pub Date : 2014-02-28 DOI:10.5121/IJCSA.2014.4104

J. SirishaDevi, Yarramalle Srinivas, S. Nandyala

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引用次数: 10

摘要

在本文中，我们提出了文本依赖的说话人识别，并使用混合FFBN和GMM方法增强了对说话人情绪的检测。说话人的情绪状态会影响识别系统。实验采用Mel-frequency倒频谱系数(MFCC)特征集。为了识别说话人的情绪状态，在训练阶段和测试阶段分别使用高斯混合模型(Gaussian Mixture Model, GMM)进行前馈-反向传播神经网络(FFBNN)。由25位说话者组成的语音数据库，记录了快乐、愤怒、悲伤、惊讶和中性五种不同的情绪状态，用于实验。结果表明，说话人的情绪状态对说话人识别的准确性有显著影响。

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Automatic Speech Emotion and Speaker Recognition Based on Hybrid GMM and FFBNN

In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a significant impact on the accuracy of speaker recognition.

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来源期刊

International Journal of Computer Science and Applications Computer Science-Computer Science Applications

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期刊介绍： IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.