{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/82395"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/82395","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Unsupervised modeling of latent topics and lexical units in speech audio","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_html":"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_has_math":false,"creators":["Harwath, David F. (David Frank)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["James R. Glass and Timothy J. Hazen."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-22T22:20:57Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/82395","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["James R. Glass and Timothy J. Hazen."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Harwath, David F. (David Frank)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-11-18T19:17:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-11-18T19:17:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2013"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/82395"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Unsupervised modeling of latent topics and lexical units in speech audio"]}]}],"canonical_facts":{"dc:contributor.advisor":["James R. Glass and Timothy J. Hazen."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Harwath, David F. (David Frank)"],"dc:date.accessioned":["2013-11-18T19:17:53Z"],"dc:date.available":["2013-11-18T19:17:53Z"],"dc:date.issued":["2013"],"dc:description":["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)."],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/82395"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Unsupervised modeling of latent topics and lexical units in speech audio"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:57Z"}