Automatic Keyword Recognition Using Hidden Markov Models
| Autor: | Shyh-Shiaw Kuo, Oscar E. Agazzi |
|---|---|
| Rok vydání: | 1994 |
| Předmět: |
business.industry
Computer science Variable-order Markov model Speech recognition Bayesian probability Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Pattern recognition Statistical model Markov model Variable-order Bayesian network Signal Processing Media Technology Computer Vision and Pattern Recognition Artificial intelligence Hidden semi-Markov model Electrical and Electronic Engineering Elastic matching business Hidden Markov model |
| Zdroj: | Journal of Visual Communication and Image Representation. 5:265-272 |
| ISSN: | 1047-3203 |
| DOI: | 10.1006/jvci.1994.1024 |
| Popis: | An algorithm for automatic recognition of keywords embedded in a poorly printed document is presented. For each keyword, two statistical models, named Hidden Markov Models (HMMs), are created to represent the actual keyword and all the other extraneous words, respectively. Dynamic programming is then used to measure the Bayesian distortions of an unknown input word with respect to the two models and making a maximum likelihood decision. The HMM facilitate a nice "elastic matching" property which makes the recognizer tolerant of highly deformed and noisy words. The system is shown to be robust, failing only when the levels of degradation are quite severe. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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