Emotion recognition by speech signals

@inproceedings{Kwon2003EmotionRB,
  title={Emotion recognition by speech signals},
  author={Oh-Wook Kwon and Kwokleung Chan and Jiucang Hao and Te-Won Lee},
  booktitle={INTERSPEECH},
  year={2003}
}
For emotion recognition, we selected pitch, log energy, formant, mel-band energies, and mel frequency cepstral coefficients (MFCCs) as the base features, and added velocity/acceleration of pitch and MFCCs to form feature streams. We extracted statistics used for discriminative classifiers, assuming that each stream is a one-dimensional signal. Extracted features were analyzed by using quadratic discriminant analysis (QDA) and support vector machine (SVM). Experimental results showed that pitch… CONTINUE READING
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