Name
Title | ||
|---|---|---|
A Novel Classifier Based on Enhanced Lipschitz Embedding for Speech Emotion Recognition |
Abstract | ||
|---|---|---|
The paper proposes a novel classifier named ELEC (Enhanced Lipschitz Embedding based Classifier) in the speech emotion recognition system. ELEC adopts geodesic distance to preserve the intrinsic geometry of speech corpus and embeds the high dimensional feature vector into a lower space. Through analyzing the class labels of the neighbor training vectors in the compressed space, ELEC classifies the test data into six archetypal emotional states, i.e. neutral, anger, fear, happiness, sadness and surprise. Experimental results on a benchmark data set demonstrate that compared with the traditional methods, the proposed classifier of ELEC achieves 17% improvement in average for speaker-independent emotion recognition and 13% for speaker-dependent. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87442-3_60 | ICIC (1) |
Keywords | Field | DocType |
enhanced lipschitz,speech corpus,test data,novel classifier,lower space,enhanced lipschitz embedding,speech emotion recognition,benchmark data,speaker-independent emotion recognition,proposed classifier,speech emotion recognition system,archetypal emotional state,geodesic distance,feature vector | Speech corpus,Computer science,Artificial intelligence,Lipschitz continuity,Classifier (linguistics),Sadness,Feature vector,Embedding,Pattern recognition,Speech recognition,Test data,Geodesic,Machine learning | Conference |
Volume | ISSN | Citations |
5226 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyu You | 1 | 160 | 16.22 |
| Guo-Zheng Li | 2 | 368 | 42.62 |
| Luonan Chen | 3 | 1485 | 145.71 |
| Jianhua Tao | 4 | 848 | 138.00 |