Speech emotion recognition using spectral entropy
- Authors
- Lee, W.-S.[Lee, W.-S.]; Roh, Y.-W.[Roh, Y.-W.]; Kim, D.-J.[Kim, D.-J.]; Kim, J.-H.[Kim, J.-H.]; Hong, K.-S.[Hong, K.-S.]
- Issue Date
- 2008
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.5315 LNAI, no.PART 2, pp.45 - 54
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 5315 LNAI
- Number
- PART 2
- Start Page
- 45
- End Page
- 54
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/82607
- DOI
- 10.1007/978-3-540-88518-4_6
- ISSN
- 0302-9743
- Abstract
- This paper proposes a Gaussian Mixture Model (GMM)-based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. © 2008 Springer Berlin Heidelberg.
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