Name
Abstract | ||
|---|---|---|
Speech signal is one of the major means for communication which carries not only semantic, but personal information, such as genders and emotions. The researches about speech emotion have become more and more important to human-computer interaction. To this end, from speech, the long-term and short-term emotional features are extracted, the dimensionality of which is then reduced by virtue of the multi linear PCA algorithm. Finally, the kernel partial least squares regression is used for speech emotional recognition. The results show that in comparison with other current classifiers, the algorithm proposed herein can improve recognition rates by about 6% to 23%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4304/jcp.9.4.998-1004 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
multi linear PCA, feature extraction, speech emotion recognition, kernel partial least squares regression | Kernel partial least squares,Regression,Pattern recognition,Computer science,Emotion recognition,Speech recognition,Curse of dimensionality,Feature extraction,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
9 | 4 | 1796-203X |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghai Xin | 1 | 55 | 5.70 |
| Weiyi Gu | 2 | 0 | 0.34 |
| Jin-Long Wang | 3 | 1402 | 94.86 |