Name
Abstract | ||
|---|---|---|
In prosody event detection field, many local acoustic features have been proposed for representing the prosody characteristics of speech unit. The context information that represents some possible regularities underlying neighboring prosody events, however, hasn't been used effectively. The main difficulty to utilize prosodic context is that it's hard to capture the long-distance sequential dependency. In order to solve this problem, we introduce a new learning approach: auto-context. In this algorithm, a classifier is first trained based on local acoustic features; the discriminative probabilities produced by the classifier are selected as context information for the next iteration. Then a new classifier is trained by using the selected context information and local acoustic features. Repeating using the updated probabilities as the context information for the next iteration, the algorithm can boost recognition ability during its iterative process until converged. The merit of this method is that it can choose context information in a flexible way, while reserving reliable context information and abandoning unreliable ones. The experimental results showed that the proposed method improved the accuracy by absolutely about 1% for pitch accent detection. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISCSLP.2012.6423523 | ISCSLP |
Keywords | Field | DocType |
discriminative probability,prosody event detection field,prosodic context,prosody,neighboring prosody events,prosody characteristics,support vector machines (svms),iterative process,speech recognition,boost recognition ability,auto-context,learning (artificial intelligence),pitch accent detection,speech unit,acoustic signal detection,long-distance sequential dependency,local acoustic features,acoustic,learning approach,automatic pitch accent detection,abandoning unreliable ones,iterative methods,probability,reliable context information,learning artificial intelligence | Prosody,Pattern recognition,Iterative and incremental development,Computer science,Iterative method,Pitch accent,Speech recognition,Artificial intelligence,Classifier (linguistics),Discriminative model | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-2505-9 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhong Zhao | 1 | 27 | 7.02 |
| Wei-Qiang Zhang | 2 | 136 | 31.22 |
| Hua Yuan | 3 | 18 | 2.66 |
| Jia Liu | 4 | 277 | 50.34 |
| Shanhong Xia | 5 | 31 | 14.64 |