Unsupervised modeling of latent topics and lexical units in speech audio
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/82395 |
| _version_ | 1826191496485797888 |
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| author | Harwath, David F. (David Frank) |
| author2 | James R. Glass and Timothy J. Hazen. |
| author_facet | James R. Glass and Timothy J. Hazen. Harwath, David F. (David Frank) |
| author_sort | Harwath, David F. (David Frank) |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T08:56:47Z |
| format | Thesis |
| id | mit-1721.1/82395 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:56:47Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/823952019-04-10T19:52:53Z Unsupervised modeling of latent topics and lexical units in speech audio Harwath, David F. (David Frank) James R. Glass and Timothy J. Hazen. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department 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, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 67-70). Zero-resource speech processing involves the automatic analysis of a collection of speech data in a completely unsupervised fashion without the benefit of any transcriptions or annotations of the data. In this thesis, we describe a zero-resource framework that automatically discovers important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic time warping (S-DTW) algorithm for acoustic pattern discovery in conjunction with a probabilistic model which treats the topic and pseudo-word identity of each discovered pattern as hidden variables. By applying an Expectation-Maximization (EM) algorithm, our method estimates the latent probability distributions over the pseudo-words and topics associated with the discovered patterns. Using this information, we produce informative acoustic summaries of the dominant topical themes of the audio document collection. by David F. Harwath. S.M. 2013-11-18T19:17:53Z 2013-11-18T19:17:53Z 2013 2013 Thesis http://hdl.handle.net/1721.1/82395 862109691 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 70 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Harwath, David F. (David Frank) Unsupervised modeling of latent topics and lexical units in speech audio |
| title | Unsupervised modeling of latent topics and lexical units in speech audio |
| title_full | Unsupervised modeling of latent topics and lexical units in speech audio |
| title_fullStr | Unsupervised modeling of latent topics and lexical units in speech audio |
| title_full_unstemmed | Unsupervised modeling of latent topics and lexical units in speech audio |
| title_short | Unsupervised modeling of latent topics and lexical units in speech audio |
| title_sort | unsupervised modeling of latent topics and lexical units in speech audio |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/82395 |
| work_keys_str_mv | AT harwathdavidfdavidfrank unsupervisedmodelingoflatenttopicsandlexicalunitsinspeechaudio |