2026-09-18T22:00:21Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/823952022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
James R. Glass and Timothy J. Hazen.
Harwath, David F. (David Frank)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-11-18T19:17:53Z
2013-11-18T19:17:53Z
2013
2013
http://hdl.handle.net/1721.1/82395
862109691
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.
70 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Unsupervised modeling of latent topics and lexical units in speech audio
Thesis
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<Abstract>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.</Abstract>
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