Hidden Markov modeling using a dominant state sequence with application to speech recognition
โ Scribed by Neri Merhav; Yariv Ephraim
- Publisher
- Elsevier Science
- Year
- 1991
- Tongue
- English
- Weight
- 898 KB
- Volume
- 5
- Category
- Article
- ISSN
- 0885-2308
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๐ SIMILAR VOLUMES
In this paper we report our development of a new class of hidden Markov models (HMMs) with each state characterized by a time series model which is non-stationary up to the second order. A closeform solution for the model parameter estimation is obtained based on the EM algorithm and on the matrix-c
A speech recogmzer ts developed usmg a layered feedforward neural network to implement speech-frame predwtlon. A Markov cham ts used to control changes in the network's wetght parameters. We postulate that speech recogmtion accuracy ts closely hnked to the capabthty of the predictive model m represe