Name
Title | ||
|---|---|---|
Multiscale kernel locally penalised discriminant analysis exemplified by emotion recognition in speech. |
Abstract | ||
|---|---|---|
We propose a novel method to learn multiscale kernels with locally penalised discriminant analysis, namely Multiscale-Kernel Locally Penalised Discriminant Analysis (MS-KLPDA). As an exemplary use-case, we apply it to recognise emotions in speech. Specifically, we employ the term of locally penalised discriminant analysis by controlling the weights of marginal sample pairs, while the method learns kernels with multiple scales. Evaluated in a series of experiments on emotional speech corpora, our proposed MS-KLPDA is able to outperform the previous research of Multiscale-Kernel Fisher Discriminant Analysis and some conventional methods in solving speech emotion recognition. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2993148.2993184 | ICMI |
Keywords | Field | DocType |
Locally penalised discriminant analysis, Multiscale kernels, Multiple kernel learning, Speech emotion | Kernel (linear algebra),Pattern recognition,Emotion recognition,Computer science,Multiple discriminant analysis,Multiple kernel learning,Kernel Fisher discriminant analysis,Speech recognition,Artificial intelligence,Linear discriminant analysis | Conference |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinzhou Xu | 1 | 1 | 0.35 |
| Jun Deng | 2 | 278 | 18.59 |
| Maryna Gavryukova | 3 | 1 | 0.35 |
| Zixing Zhang | 4 | 397 | 31.73 |
| Li Zhao | 5 | 6 | 2.16 |
| Björn Schuller | 6 | 6749 | 463.50 |