Name
Abstract | ||
|---|---|---|
We report our recent work on noise-robust large-vocabulary speech recognition. Three key innovations are developed and evaluated in this work: 1) a new model learning paradigm that comprises a noise-insertion process followed by noise reduction; 2) a noise adaptive training algorithm that integrates noise reduction into probabilistic multi-style system training; and 3) a new algorithm (SPLICE) for noise reduction that makes no assumptions about noise stationarity. Evaluation on a large-vocabulary speech recognition task demonstrates significant and consistent error rate reduction using these techniques. The resulting error rate is shown to be lower than that achieved by the matched-noisy condition for both stationary and nonstationary natural, as well as simulated, noises. |
Year | Venue | Keywords |
|---|---|---|
2000 | INTERSPEECH | noise reduction,speech recognition,error rate |
Field | DocType | Citations |
Noise reduction,Vocabulary speech recognition,Pattern recognition,Voice activity detection,Computer science,Word error rate,Speech recognition,Artificial intelligence,Probabilistic logic,Model learning | Conference | 106 |
PageRank | References | Authors |
10.90 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deng, Li | 1 | 9691 | 728.14 |
| A. Acero | 2 | 4390 | 478.73 |
| mike plumpe | 3 | 208 | 27.43 |
| Xuedong Huang | 4 | 1390 | 283.19 |