Initial Model Study of Speech Emotion Recognition Using Hidden Markov Model Based System

  • 黃 俊修

Student thesis: Doctoral Thesis

Abstract

Emotion recognition from speech signal is one of the most important topics in human-machine interaction it is used for the chatting bot mental examination safety warning …etc For the past few years there has been tried for several speech features and classifier e g pitch formant Mel-frequency Cepstral Coefficient (MFCC) features and Support Vector Machine (SVM) Gaussian Mixture Model (GMM) Hidden Markov Model (HMM) Artificial Neuron Network (ANN) classifiers Emotional speech can be regarded as a prosody state flow In this work the accuracy results among GMM discrete HMM and continuous HMM are compared which are used the MFCC as speech features There are also underflow and singularity problems among the above systems it will be discussed and overcome Moreover the pre-processing of initial model hypothesis for HMM classifier will be discussed in this paper Finally the highest accuracy results of recognition are 58 07% 65 67% 89 20% for GMM discrete HMM continuous HMM classifiers respectively and the highest average accuracy results of recognition are 51 12% 53 20% 70 15% for GMM discrete HMM continuous GMM classifiers respectively The results show that continuous HMM are the best classifier among them And it supports that the accuracy used HMM considering state flow outperforms GMM which is considering only statistical information Moreover it also supports that the continuous HMM which is used multivariant dimension probability density outperforms the discrete HMM used discrete probability
Date of Award2019
Original languageEnglish
SupervisorLih-Yih Chiou (Supervisor)

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