Deep Learning Based Emotion Recognition from Chinese Speech

Emotion Recognition is challenging for understanding people and enhance human computer interaction experiences. In this paper, we explore deep belief networks DBN to classify six emotion status: anger, fear, joy, neutral status, sadness and surprise using different features fusion. Several kinds of speech features such as Mel frequency cepstrum coefficient MFCC, pitch, formant, et al., were extracted and combined in different ways to reflect the relationship between feature combinations and emotion recognition performance. We adjusted different parameters in DBN to achieve the best performance when solving different emotions. Both gender dependent and gender independent experiments were conducted on the Chinese Academy of Sciences emotional speech database. The highest accuracy was 94.6i?ź%, which was achieved using multi-feature fusion. The experiment results show that DBN based approach has good potential for practical usage of emotion recognition, and suitable multi-feature fusion will improve the performance of speech emotion recognition.

[1]  Tiago H. Falk,et al.  Automatic speech emotion recognition using modulation spectral features , 2011, Speech Commun..

[2]  Björn W. Schuller,et al.  Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Wei Chen,et al.  Speech Emotion Recognition Based on EMD in Noisy Environments , 2013 .

[4]  Fakhri Karray,et al.  Survey on speech emotion recognition: Features, classification schemes, and databases , 2011, Pattern Recognit..

[5]  Geoffrey E. Hinton,et al.  Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Rosalind W. Picard.  Affective Computing , 1997 .

[7]  Rosalind W. Picard.  Affective computing: (526112012-054) , 1997 .

[8]  Akshay S. Utane,et al.  Emotion Recognition through Speech Using Gaussian Mixture Model and Support Vector Machine , 2013 .

[9]  K. Stevens,et al.  Emotions and speech: some acoustical correlates. , 1972, The Journal of the Acoustical Society of America.

[10]  Carlos Busso,et al.  Emotion recognition using a hierarchical binary decision tree approach , 2011, Speech Commun..

[11]  D. Mitchell Wilkes,et al.  Acoustical properties of speech as indicators of depression and suicidal risk , 2000, IEEE Transactions on Biomedical Engineering.

[12]  Xiong Chen,et al.  Automatic Speech Emotion Recognition using Support Vector Machine , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[13]  Geoffrey E. Hinton.  Deep belief networks , 2009, Scholarpedia.

[14]  K. Scherer,et al.  Acoustic profiles in vocal emotion expression. , 1996, Journal of personality and social psychology.

[15]  Chengxin Li,et al.  Speech emotion recognition with acoustic and lexical features , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Honglak Lee,et al.  Deep learning for robust feature generation in audiovisual emotion recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.