Abstract
Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.
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Acknowledgements
The research is supported partially by the National Basic Research Program of China (No. 2013CB329303), and the National Natural Science Foundation of China (No. 61503277 and No. 61303109). The study is supported partially by JSPS KAKENHI Grant (16K00297).
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Guan, H., Liu, Z., Wang, L., Dang, J., Yu, R. (2018). Speech Emotion Recognition Considering Local Dynamic Features. In: Fang, Q., Dang, J., Perrier, P., Wei, J., Wang, L., Yan, N. (eds) Studies on Speech Production. ISSP 2017. Lecture Notes in Computer Science(), vol 10733. Springer, Cham. https://doi.org/10.1007/978-3-030-00126-1_2
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