Name
Abstract | ||
|---|---|---|
The tone is a distinctive discriminative feature in Mandarin Chinese. Often functional, yet seldom thorough are most large-scale Mandarin speech recognition systems in treating tone modeling. In particular, many lack the necessary sophistication to deal with the myriad variations arising from the combination of acoustic and lexical contexts. This paper reports an attempt to account for these variabilities and to bring richer tone modeling into the IBM Mandarin broadcast transcription system. In particular, we describe a system that combines the embedded approach and a novel explicit tone modeling technique characterized by a. robust tone tracking in the main-vowel domain, and b. context-dependent models with lexical and prosodic contexts. The proposed method is validated on a connected-digits set and subsequently evaluated on a large-vocabulary broadcast transcription task. It is shown that 14.8% and 5.4% relative reductions in character error rate are achieved respectively. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4960645 | ICASSP |
Keywords | Field | DocType |
main vowel domain tone,character error rate,richer tone modeling,prosodic analysis,ibm mandarin broadcast transcription,large-vocabulary broadcast transcription task,novel explicit tone modeling,lexical context,tone modeling,large-scale mandarin speech recognition,mandarin chinese,robust tone tracking,mandarin asr,broadcasting,robustness,decision tree,natural languages,lexical analysis,context dependent,natural language processing,decision trees,context modeling,automatic speech recognition,lattices,feature extraction,acoustics,speech,speech synthesis,speech recognition,parameter estimation | Speech synthesis,Computer science,Word error rate,Speech recognition,Context model,Natural language,Natural language processing,Vowel,Artificial intelligence,Lexical analysis,Discriminative model,Mandarin Chinese | Conference |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.52 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shilei Zhang | 1 | 57 | 9.81 |
| Qin Shi | 2 | 61 | 10.77 |
| Stephen M. Chu | 3 | 372 | 26.33 |
| Yong Qin | 4 | 161 | 42.54 |