Training approach for hidden Markov models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1554-1555 |
| Journal / Publication | Electronics Letters |
| Volume | 32 |
| Issue number | 17 |
| Publication status | Published - 1996 |
Link(s)
Abstract
The authors propose a new training approach based on maximum model distance (MMD) for HMMs. MMD uses the entire training set to estimate the parameters of each HMM, while the traditional maximum likelihood (ML) only uses those data labelled for the model. Experimental results showed that significant error reduction can be achieved through the proposed approach. In addition, the relationship between MMD and corrective training [3] was discussed, and we have proved that the corrective training is a special case of MMD approach.
Research Area(s)
- Hidden Markov models, Maximum likelihood estimation, Speech recognition
Citation Format(s)
Training approach for hidden Markov models. / Kwong, S.; He, Q. H.; Man, K. F.
In: Electronics Letters, Vol. 32, No. 17, 1996, p. 1554-1555.
In: Electronics Letters, Vol. 32, No. 17, 1996, p. 1554-1555.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
