Name
Abstract | ||
|---|---|---|
Emotion recognition based on a speech signal is one of the intensively studied research topics in the domains of human-computer interaction and affective computing. The presented paper is concerned with emotional-speech recognition based on the neuro-fuzzy network with a weighted fuzzy membership function (NEWFM). NEWFM has a feature selection method and makes fuzzy classifiers. In this paper, NEWFM was utilized for classifying four kinds of emotional-speech signals. This NEWFM classification method achieves as high as 86% overall classification accuracy. Significantly, the NEWFM classifier efficiently detects sadness, with a 97.5% recognition rate. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2184751.2184863 | ICUIMC |
Keywords | Field | DocType |
emotion recognition,fuzzy classifier,feature selection method,emotional-speech recognition,newfm classification method,recognition rate,overall classification accuracy,emotional-speech signal,newfm classifier,neuro-fuzzy network,weighted fuzzy membership function,speech recognition,human computer interaction,neuro fuzzy,affective computing,feature selection | Neuro-fuzzy,Feature selection,Pattern recognition,Emotion recognition,Computer science,Fuzzy logic,Fuzzy membership function,Speech recognition,Artificial intelligence,Affective computing,Fuzzy classifier,Classifier (linguistics) | Conference |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Murlikrishna Viswanathan | 1 | 21 | 6.30 |
| Zhen-Xing Zhang | 2 | 6 | 4.13 |
| Xue-Wei Tian | 3 | 1 | 1.70 |
| Joon S. Lim | 4 | 99 | 12.15 |