Pronunciation learning for automatic speech recognition
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2011
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| Online Access: | http://hdl.handle.net/1721.1/66022 |
| _version_ | 1811093216127090688 |
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| author | Badr, Ibrahim |
| author2 | James Glass. |
| author_facet | James Glass. Badr, Ibrahim |
| author_sort | Badr, Ibrahim |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T15:41:30Z |
| format | Thesis |
| id | mit-1721.1/66022 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:41:30Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/660222019-04-12T11:20:59Z Pronunciation learning for automatic speech recognition Learning pronunciation for automatic speech recognition Badr, Ibrahim James Glass. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 99-101). In many ways, the lexicon remains the Achilles heel of modern automatic speech recognizers (ASRs). Unlike stochastic acoustic and language models that learn the values of their parameters from training data, the baseform pronunciations of words in an ASR vocabulary are typically specified manually, and do not change, unless they are edited by an expert. Our work presents a novel generative framework that uses speech data to learn stochastic lexicons, thereby taking a step towards alleviating the need for manual intervention and automnatically learning high-quality baseform pronunciations for words. We test our model on a variety of domains: an isolated-word telephone speech corpus, a weather query corpus and an academic lecture corpus. We show significant improvements of 25%, 15% and 2% over expert-pronunciation lexicons, respectively. We also show that further improvements can be made by combining our pronunciation learning framework with acoustic model training. by Ibrahim Badr. S.M. 2011-09-27T18:33:33Z 2011-09-27T18:33:33Z 2011 2011 Thesis http://hdl.handle.net/1721.1/66022 751988889 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 101 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Badr, Ibrahim Pronunciation learning for automatic speech recognition |
| title | Pronunciation learning for automatic speech recognition |
| title_full | Pronunciation learning for automatic speech recognition |
| title_fullStr | Pronunciation learning for automatic speech recognition |
| title_full_unstemmed | Pronunciation learning for automatic speech recognition |
| title_short | Pronunciation learning for automatic speech recognition |
| title_sort | pronunciation learning for automatic speech recognition |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/66022 |
| work_keys_str_mv | AT badribrahim pronunciationlearningforautomaticspeechrecognition AT badribrahim learningpronunciationforautomaticspeechrecognition |