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Currently, there is a lack of voice samples in the speech emotion recognition field, which leads to poor recognition rate and over-fitting of data. Inspire by this, we propose speech emotion recognition based on data enhancement. The Berlin Emotional Corpus is enhanced from two directions: Time Domain and Frequency Domain. The samples was extracted and trained. Research and analyze the recognition rate of two classifiers: K-Nearest Neighbor and Support Vector Machine. Experiments show that the effect after data enhancement is better.
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Qianqian Li, Fuji Ren, Xiaoyan Shen, Xin Kang, "Speech emotion recognition based on data enhancement in time-frequency domain," Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740R (12 October 2020); https://doi.org/10.1117/12.2579205