Young, SJ (1990) Competitive training in hidden Markov models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2. pp. 681-684. ISSN 0736-7791Full text not available from this repository.
The use of hidden Markov models is placed in a connectionist framework, and an alternative approach to improving their ability to discriminate between classes is described. Using a network style of training, a measure of discrimination based on the a posteriori probability of state occupation is proposed, and the theory for its optimization using error back-propagation and gradient ascent is presented. The method is shown to be numerically well behaved, and results are presented which demonstrate that when using a simple threshold test on the probability of state occupation, the proposed optimization scheme leads to improved recognition performance.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 18:03|
|Last Modified:||20 Feb 2017 05:30|