Abstract
Recognizing emotional state from human voice is one of the important issues on speech signal processing. In this paper, we use the dNMF algorithm to find emotion-related spectral components in word speech. Each word consists of only vowels to remove language-dependent emotional factors. The dNMF algorithm with the additional Fisher criterion on the cost function of conventional NMF was designed to increase class-related discriminating power. Our experiment to recognize happiness, sadness, anger, and boredom in vowel sounds shows that more informative harmonic structures are computed by dNMF than NMF. Furthermore, dNMF features result in better recognition rates than NMF features for speaker-independent emotion recognition.
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Kim, BK., Lee, SY. (2013). Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_59
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DOI: https://doi.org/10.1007/978-3-642-42051-1_59
Publisher Name: Springer, Berlin, Heidelberg
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