Name
Abstract | ||
|---|---|---|
In conventional speech processing, researchers adopt a dividable assumption, that the speech utterance can be divided into non-overlapping feature sequences and each segment represents an acoustic event or a label. And the probability of a label sequence on an utterance approximates to the probability of the best utterance segmentation for this label sequence. But in the real case, feature sequences of acoustic events may be overlapped partially, especially for the neighboring phonemes within a syllable. And the best segmentation approximation even reinforces the distortion by the dividable assumption. In this paper, we propose an all-path decoding algorithm, which can fuse the information obtained by different segmentations (or paths) without paying obvious computation load, so the weakness of the dividable assumption could be alleviated. Our experiments show, the new decoding algorithm can improve the system performance effectively in tasks with heavy insertion and deletion errors. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11939993_46 | ISCSLP |
Keywords | Field | DocType |
speech recognition,conventional speech processing,all-path decoding algorithm,feature sequence,utterance segmentation,label sequence,speech utterance,different segmentation,acoustic event,dividable assumption,best segmentation approximation,system performance,speech processing | Speech processing,Computer science,Utterance,Artificial intelligence,Distortion,Pattern recognition,Segmentation,Algorithm,Speech recognition,Sensor fusion,Natural language,Syllable,Decoding methods | Conference |
Volume | ISSN | ISBN |
4274 | 0302-9743 | 3-540-49665-3 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||