N2 - 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. L1 - http://journals.pan.pl/Content/101549/PDF/02_paper.pdf L2 - http://journals.pan.pl/Content/101549 PY - 2013 IS - No 4 EP - 463 DO - 10.2478/aoa-2013-0054 KW - speech emotion recognition KW - speech enhancement KW - emotion model KW - Gaussian mixture model A1 - Chengwei Huang A1 - Chen, Guoming A1 - Hua Yu A1 - Yongqiang Bao A1 - Li Zhao PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 38 DA - 2013 T1 - Speech Emotion Recognition under White Noise SP - 457 UR - http://journals.pan.pl/dlibra/publication/edition/101549 T2 - Archives of Acoustics