Name
Abstract | ||
|---|---|---|
The authors propose a continuous-speech recognition method that uses an accurate and efficient parsing mechanism, an LR parser, and drives HMM (hidden Markov model) modules directly without any intervening structures such as a phoneme lattice. The method was tested in Japanese phrase recognition experiments. Two grammars were prepared, a general Japanese grammar and a task-specific grammar. The phrase recognition rate with the general grammar was 72% for top candidates and 95% for the five best candidates. With the task-specific grammar, recognition rate was 80% and 99% respectively |
Year | DOI | Venue |
|---|---|---|
1989 | 10.1109/ICASSP.1989.266524 | Glasgow |
Keywords | DocType | ISSN |
hmm continuous speech recognition,speech recognition,predictive lr parsing,task-specific grammar,japanese phrase recognition,markov processes,hidden markov model,computer languages,context modeling,lattices,natural languages,mars,testing,telephony,hidden markov models | Conference | 1520-6149 |
Citations | PageRank | References |
32 | 5.79 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kita, K. | 1 | 32 | 5.79 |
| Takeshi Kawabata | 2 | 296 | 51.73 |
| Saito, H. | 3 | 53 | 9.13 |