Name
Abstract | ||
|---|---|---|
This paper presents a novel bilingual model modification approach to improve nonnative speech recognition accuracy when the variations of accented pronunciations occur. Each state of baseline nonnative acoustic model is modified with several candidate states from the auxiliary acoustic model, which is trained on speakers' mother language. State mapping criterion and n-best candidates are investigated, and different numbers of Gaussian mixtures of the auxiliary acoustic model are compared based on a grammar-constrained speech recognition system. Using this bilingual model modification approach, compared to the nonnative acoustic model which has already been well trained by adaptation technique MAP, the Phrase Error Rate further achieves a 5.83% relative reduction, while only a small relative increase on Real Time Factor occurs. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FUZZY.2009.5277103 | FUZZ-IEEE |
Keywords | Field | DocType |
grammar-constrained speech recognition system,baseline nonnative acoustic model,candidate state,nonnative acoustic model,nonnative speech recognition accuracy,small relative increase,nonnative speech recognition,bilingual model modification approach,auxiliary acoustic model,novel bilingual model modification,relative reduction,databases,computational modeling,natural language processing,real time factor,real time,hidden markov models,acoustics,error rate,speech recognition,gaussian processes,speech | Real time factor,Computer science,Word error rate,Phrase,Speech recognition,Gaussian,Artificial intelligence,Gaussian process,Natural language processing,Hidden Markov model,First language,Acoustic model | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingqing Zhang | 1 | 102 | 14.76 |
| Jielin Pan | 2 | 44 | 18.04 |
| Shui-duen Chan | 3 | 5 | 1.57 |
| Yonghong Yan | 4 | 656 | 114.13 |