Abstract
Speech is an easy and useful way to detect speakers’ mental and psychological health, and automatic emotion recognition in speech has been investigated widely in the fields of human-machine interaction, psychology, psychiatry, etc. In this paper, we extract prosodic and spectral features including pitch, MFCC, intensity, ZCR and LSP to establish the emotion recognition model with SVM classifier. In particular, we find different frame duration and overlap have different influences on final results. So, Depth-First-Search method is applied to find the best parameters. Experimental results on two known databases, EMODB and RAVDESS, show that this model works well, and our speech features are enough effectively in characterizing and recognizing emotions.
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Acknowledgments
The research was supported in part by NSFC under Grants 11301504 and U1536104, in part by National Basic Research Program of China (973 Program2014CB744600).
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Gao, Y., Li, B., Wang, N., Zhu, T. (2017). Speech Emotion Recognition Using Local and Global Features. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_1
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DOI: https://doi.org/10.1007/978-3-319-70772-3_1
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