Name
Abstract | ||
|---|---|---|
Recognition of voice in a multi-speaker environment involves speech separation, speech feature extraction and speech feature matching. Though traditionally vector quantization is one of the algorithms used for speaker recognition; its effectiveness is not well appreciated in case of noisy or multi-speaker environment. This paper describes the usability of the Independent Component Analysis (ICA) technique to enhance the effectiveness of speaker recognition using vector quantization. Results obtained by this approach are compared with that obtained using a more direct approach to establish the usefulness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11590316_38 | PReMI |
Keywords | Field | DocType |
vector quantization,independent component analysis,hybrid approach,multi-speaker environment,speaker recognition,speech separation,speech feature extraction,speech feature matching,direct approach,speech recognition,feature extraction | Mel-frequency cepstrum,Pattern recognition,Computer science,Usability,Feature extraction,Speech recognition,Speaker recognition,Feature (machine learning),Vector quantization,Independent component analysis,Artificial intelligence,Speaker diarisation | Conference |
Volume | ISSN | ISBN |
3776 | 0302-9743 | 3-540-30506-8 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jigish Trivedi | 1 | 0 | 0.34 |
| Anutosh Maitra | 2 | 0 | 1.01 |
| Suman K. Mitra | 3 | 76 | 22.81 |