Name
Abstract | ||
|---|---|---|
An emotion recognition system based on BP neural network to recognize special human affective states existed in the speech signal is presented in this paper. About 600 short sentences with different contents in different emotional speeches from 4 speakers are collected for training and testing the feasibility of the system. The energy, pitch and speech rate characteristics are extracted from these speech signals. angry, calm, happy, sad, and surprise as the 5 typical emotions are classified with BP Neural network. In order to update automatically the emotion recognition system with time, an additional study step, just as the feed-back control, is adopted to train the finished network again according to the output. The experiments show that the system is of satisfactory emotion detection performance for some emotions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87734-9_91 | ISNN (2) |
Keywords | Field | DocType |
different content,speech emotion recognition system,typical emotion,speech rate characteristic,different emotional speech,speech signal,bp neural network,finished network,emotion recognition system,matlab environment,additional study step,satisfactory emotion detection performance,neural network | MATLAB,Computer science,Emotion recognition,Speech recognition,Time delay neural network,Emotion detection,Artificial intelligence,Surprise,Affect (psychology),Artificial neural network | Conference |
Volume | ISSN | Citations |
5264 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guobao Zhang | 1 | 37 | 8.04 |
| Qinghua Song | 2 | 1 | 1.72 |
| Shu-Min Fei | 3 | 1150 | 96.93 |