Abstract
In this correspondence, a segmental probability model (SPM) is proposed for fast and accurate recognition of the highly confusing isolated Mandarin base-syllables by deleting the state transition probabilities of continuous density hidden Markov models (CHMM), abandoning the dynamic programming process, letting the states equally segment the base-syllables deterministically, and using several special approaches to improve the accuracy and speed. This is achieved by considering the special characteristics of the target vocabulary.
Original language | English |
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Pages (from-to) | 293-299 |
Number of pages | 7 |
Journal | IEEE Transactions on Speech and Audio Processing |
Volume | 6 |
Issue number | 3 |
DOIs | |
State | Published - 1998 |
Keywords
- Hidden markov model
- Isolated syllable
- Mandarin chinese
- Segmental probability model
- Speech recognition