Abstract
A new pattern matching method, Partly Hidden Markov Model, is proposed and applied to speech recognition. Hidden Markov Model, which is widely used for speech recognition, can deal with only piecewise stationary stochastic process. We solved this problem by introducing the modified second order Markov Model, in which the first state is hidden and the second one is observable. In this model, not only the feature parameter observations but also the state transitions are dependent on the previous feature observation. Therefore, even the complicated transient can be modeled precisely. Some simulational experiments showed the high potential of the proposed model. As the results of word recognition test, the error rate was reduced by 39% compared with normal HMM.
| Original language | English |
|---|---|
| Pages (from-to) | 121-124 |
| Number of pages | 4 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Volume | 1 |
| Publication status | Published - 1999 Jan 1 |
| Event | Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA Duration: 1999 Mar 15 → 1999 Mar 19 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering