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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nopsuwanchai, R. Biem, A. |
| Copyright Year | 2003 |
| Description | Author affiliation: Comput. Lab., Cambridge Univ., UK (Nopsuwanchai, R.) |
| Abstract | This paper describes and evaluates the maximum mutual information criterion (MMI) for online unconstrained-style handwritten digit recognition based on hidden Markov models (HMMs). The study focuses on determining the best MMI optimization scheme and the HMM parameters that exhibit the most discriminative capabilities in the context of tied mixture density hidden Markov models (TDHMMs), where all HMM states share a pool of Gaussians. The experimental results show that the second-order optimization scheme is the most efficient and that although means and covariance matrix are shared by all models, they contribute the most to discrimination. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 289799 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1202492 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Handwriting recognition Covariance matrix Personal digital assistants Mutual information Maximum likelihood estimation Laboratories Handheld computers Hardware Power system modeling |
| Content Type | Text |
| Resource Type | Article |
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