Name
Abstract | ||
|---|---|---|
In this paper, a new common component GMM (CCGMM)based speaker recognition approach is presented. It first defines a divergence measure to calculate the similarity of the speech signals of two speakers. Then, a CCGMM training algorithm which simultaneously maximizes the likelihood of CCGMM and the inter-speaker divergence is proposed. Performance of the proposed approach was examined using a telephone-speech database (MAT) containing 2962 speakers. A speaker recognition rate of 90.0% was achieved. The recognition rate raised to 96.1% when it was combined with the conventional GMM-based scheme. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1415196 | 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING |
Keywords | Field | DocType |
entropy,speech,databases,computational complexity,probability distribution,signal generators,random variables,maximum likelihood estimation,speaker recognition,gaussian distribution | Random variable,Divergence,Pattern recognition,Computer science,Signal generator,Maximum likelihood,Speech recognition,Speaker recognition,Probability distribution,Speaker diarisation,Artificial intelligence,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yih-Ru Wang | 1 | 237 | 34.68 |
| Chen-Yu Chiang | 2 | 31 | 11.55 |