Name
Abstract | ||
|---|---|---|
Posterior probability is mostly used for pronunciation evaluation. This paper introduces pronunciation space models to calculate posterior probability replacing traditional phone-based acoustic models, which makes the calculated posterior probability more precise. Pronunciation space models are constructed using unsupervised clustering method guided by human scores and phone-level posterior probability. By using correlation between machine scores and human scores as the performance measurement, pronunciation space models based method shows its effectiveness for pronunciation evaluation in the experiments on a Chinese database spoken by Koreans with the correlation's improvement from 0.390 to 0.415 comparing to the traditional method based on phone based acoustic models. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CHINSL.2008.ECP.17 | ISCSLP |
Keywords | Field | DocType |
speech recognition,machine scores,chinese database,posterior probability,human scores,pronunciation space models,index terms— pronunciation evaluation,phone-level posterior probability,pronunciation evaluation,phone-based acoustic models,natural language processing,unsupervised clustering method,probability,indexing terms,hidden markov models,acoustics,correlation,speech,databases | Pronunciation,Pattern recognition,Computer science,Posterior probability,Speech recognition,Phone,Correlation,Artificial intelligence,Natural language processing,Cluster analysis,Hidden Markov model | Conference |
ISBN | Citations | PageRank |
978-1-4244-2943-1 | 0 | 0.34 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Si Wei | 1 | 0 | 0.34 |
| Yi-Qian Pan | 2 | 0 | 0.34 |
| Guoping Hu | 3 | 309 | 37.32 |
| Yu Hu | 4 | 537 | 76.69 |
| Ren-Hua Wang | 5 | 344 | 41.36 |