Revisiting Hidden Markov Models for Speech Emotion Recognition
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要Hidden Markov models (HMMs) have a long tradition in automatic speech recognition (ASR) due to their capability of capturing temporal dynamic characteristics of speech. For emotion recognition from speech, three HMM based architectures are investigated and com-pared throughout the current paper, namely, the Gaussian mixture model based HMMs (GMM-HMMs), the subspace based Gaussian mixture model based HMMs (SGMM-HMMs) and the hybrid deep neural network HMMs (DNN-HMMs). Extensive emotion recognition experiments are carried out on these three architectures on the CASIA corpus, the Emo-DB corpus and the IEMOCAP database, respectively, and results are compared with those of state-of-the-art approaches. These HMM based architectures prove capable of constituting an effective model for speech emotion recognition. Also, the modeling accuracy is further enhanced by incorporating various advanced techniques from the ASR area. In particular, among all of the architectures, the SGMM-HMMs achieve the best performance in most of the experiments.
出版社接受日期02.02.2019
著者Shuiyang MAO, Dehua TAO, Guangyan ZHANG, P.C. CHING, Tan LEE
會議名稱44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
會議開始日12.05.2019
會議完結日17.05.2019
會議地點Brighton
會議國家/地區英國
會議論文集題名Proceedings ICASSP 2019
出版年份2019
月份5
日期12
出版社IEEE
出版地USA
頁次6715 - 6719
國際標準書號978-1-4799-8131-1
國際標準期刊號1520-6149
語言英式英語
關鍵詞Speech emotion recognition, hidden Markov models, subspace based GMM, hybrid DNN-HMM

上次更新時間 2020-21-10 於 02:53