Abstract
Emotion recognition from voice has recently attracted considerable interest in the fields of human-machine communication. In this paper, we propose an emotion recognition system which is a combination of three subsystems. The first and second subsystems utilize support vector machines (SVM) and deep neural networks (DNN) respectively to classify the features directly. In the third subsystem, we utilize DNN to extract segment-level features from raw data and show that they are effective for speech emotion recognition. The extracted segment-level features are emotion state probability distribution. Then we construct utterance-level features from segment-level probability distributions. Finally, utterance-level features are fed into a SVM to identify the emotions for each utterance. The experimental results show that all the subsystems outperform the hidden markov model (HMM) baseline, and the combined system get the best performance on F-score.
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Acknowledgements
This work was supported by the China NSFC projects (No. 61603252 and No. U1736202), the Shanghai Sailing Program No. 16YF1405300, and the Tencent-Shanghai Jiao Tong University joint project. Experiments have been carried out on the PI supercomputer at Shanghai Jiao Tong University. We would like to thank Heinrich Dinkel for his insightful comments on this paper.
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Chen, R., Zhou, Y., Qian, Y. (2018). Emotion Recognition Using Support Vector Machine and Deep Neural Network. In: Tao, J., Zheng, T., Bao, C., Wang, D., Li, Y. (eds) Man-Machine Speech Communication. NCMMSC 2017. Communications in Computer and Information Science, vol 807. Springer, Singapore. https://doi.org/10.1007/978-981-10-8111-8_12
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DOI: https://doi.org/10.1007/978-981-10-8111-8_12
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