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- W4385992753 abstract "Many resources and tools on emotion recognition have been prepared and developed by academia for a long time. Resources are a prerequisite for emotion recognition implementation. This paper reviews the research status of commonly used speech emotion databases in detail. Since the extraction of speech emotion features has a decisive influence on the recognition results, the selection of its features is the top priority of the research. Based on this, this paper proposes a method on speech emotion recognition based on SVM multi-feature fusion. The five features of MFCC, Melspectrogram, Chroma, Tonnetz and Contrast are used for fusion. The experimental results show that the recognition rate of the fusion feature paper proposed in this paper reaches 90.15%. When using the SVM classifier on the emo-db database. It can be concluded that multi-feature fusion has better recognition performance than single feature, and the performance is stable. Finally, the current status and future trends of speech emotion recognition are discussed." @default.
- W4385992753 created "2023-08-19" @default.
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- W4385992753 date "2022-03-01" @default.
- W4385992753 modified "2023-10-14" @default.
- W4385992753 title "Research on Speech Emotion Recognition Base on SVM" @default.
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- W4385992753 doi "https://doi.org/10.1109/icitbs55627.2022.00060" @default.
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