Name
Abstract | ||
|---|---|---|
The robustness of phoneme recognition using support vector ma- chines to additive noise is investigated for three kinds of speech representation. The representations considered are PLP, PLP with RASTA processing, and a high-dimensional principal component approximation of acoustic waveforms. While the classification in the PLP and PLP/RASTA domains attains superb accuracy on clean data, the classification in the high-dimensional space proves to be much more robust to additive noise. |
Year | Venue | Field |
|---|---|---|
2006 | EUSIPCO | Pattern recognition,Support vector machine,Speech recognition,Robustness (computer science),Artificial intelligence,Phoneme recognition,Principal component analysis,Mathematics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lena Khoo | 1 | 0 | 0.34 |
| Zoran Cvetkovi | 2 | 0 | 0.34 |
| Peter Sollich | 3 | 298 | 38.11 |