Speech emotion recognition under white noise

• Autorzy: Huang C. , Chen G. , Yu H. , Bao Y. , Zhao L.
• Języki publikacji: EN

Abstrakty

EN

Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.

Słowa kluczowe

EN

speech emotion recognition; speech enhancement; emotion model; Gaussian mixture model

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2013
• Tom: Vol. 38, No. 4
• Strony: 457--463
• Opis fizyczny: Bibliogr. 27 poz., tab., wykr.

Twórcy

autor

Huang C.

• School of Information Science and Engineering Southeast University 2# Sipailou, Nanjing 210096, Jiangsu Prov., China

autor

Chen G.

• School of Information Science and Engineering Southeast University 2# Sipailou, Nanjing 210096, Jiangsu Prov., China

autor

Yu H.

• School of Information Science and Engineering Southeast University 2# Sipailou, Nanjing 210096, Jiangsu Prov., China

autor

Bao Y.

• School of Communication Engineering Nanjing Institute of Technology 1# Hongjing Ave., Nanjing 211167, Jiangsu Prov., China

autor

Zhao L.

• School of Information Science and Engineering Southeast University 2# Sipailou, Nanjing 210096, Jiangsu Prov., China

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