Speech emotion recognition using spectral entropy
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, W.-S.[Lee, W.-S.] | - |
dc.contributor.author | Roh, Y.-W.[Roh, Y.-W.] | - |
dc.contributor.author | Kim, D.-J.[Kim, D.-J.] | - |
dc.contributor.author | Kim, J.-H.[Kim, J.-H.] | - |
dc.contributor.author | Hong, K.-S.[Hong, K.-S.] | - |
dc.date.accessioned | 2021-08-07T18:42:51Z | - |
dc.date.available | 2021-08-07T18:42:51Z | - |
dc.date.created | 2017-01-12 | - |
dc.date.issued | 2008 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/82607 | - |
dc.description.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. | - |
dc.subject | Entropy | - |
dc.subject | Face recognition | - |
dc.subject | Fast Fourier transforms | - |
dc.subject | Filter banks | - |
dc.subject | Fourier transforms | - |
dc.subject | Magnetostrictive devices | - |
dc.subject | Robotics | - |
dc.subject | Speech | - |
dc.subject | Emotion recognitions | - |
dc.subject | Feature parameters | - |
dc.subject | Frequency filters | - |
dc.subject | Gaussian Mixture models | - |
dc.subject | Linear Prediction coefficients | - |
dc.subject | Spectral entropies | - |
dc.subject | Speech databases | - |
dc.subject | Speech emotion recognitions | - |
dc.subject | Zero crossing rates | - |
dc.subject | Speech recognition | - |
dc.title | Speech emotion recognition using spectral entropy | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, W.-S.[Lee, W.-S.] | - |
dc.contributor.affiliatedAuthor | Roh, Y.-W.[Roh, Y.-W.] | - |
dc.contributor.affiliatedAuthor | Kim, D.-J.[Kim, D.-J.] | - |
dc.contributor.affiliatedAuthor | Kim, J.-H.[Kim, J.-H.] | - |
dc.contributor.affiliatedAuthor | Hong, K.-S.[Hong, K.-S.] | - |
dc.identifier.doi | 10.1007/978-3-540-88518-4_6 | - |
dc.identifier.scopusid | 2-s2.0-56749130825 | - |
dc.identifier.bibliographicCitation | 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 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 5315 LNAI | - |
dc.citation.number | PART 2 | - |
dc.citation.startPage | 45 | - |
dc.citation.endPage | 54 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
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