{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119706"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119706","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A study of adaptive enhancement methods for improved distant speech recognition","abstract":"Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech.","abstract_html":"Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech.","abstract_has_math":false,"creators":["Titus, Andrew Richard"],"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 Glass and Hao Tang."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:21:30Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/119706","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["James Glass and Hao Tang."]},{"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":["Titus, Andrew Richard"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-12-18T19:46:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-12-18T19:46:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2018"]},{"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":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written 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/1721.1/119706"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 65-68)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["A study of adaptive enhancement methods for improved distant speech recognition"]}]}],"canonical_facts":{"dc:contributor.advisor":["James Glass and Hao Tang."],"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":["Titus, Andrew Richard"],"dc:date.accessioned":["2018-12-18T19:46:36Z"],"dc:date.available":["2018-12-18T19:46:36Z"],"dc:date.issued":["2018"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 65-68)."],"dc:description.abstract":["Automatic speech recognition systems trained on speech data recorded by microphones placed close to the speaker tend to perform poorly on speech recorded by microphones placed farther away from the speaker due to reverberation effects and background noise. I designed and implemented a variety of machine learning models to improve distant speech recognition performance by adaptively enhancing incoming speech to appear as if it was recorded in a close-talking environment, regardless of whether it was originally recorded in a close-talking or distant environment. These were evaluated by passing the enhanced speech to acoustic models trained on only close-talking speech and comparing error rates to those achieved without speech enhancement. Experiments conducted on the AMI, TIMIT and TED-LIUM datasets indicate that decreases in error rate on distant speech of up to 33% relative can be achieved by these with only minor increases (1% relative) on clean speech."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119706"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["A study of adaptive enhancement methods for improved distant speech recognition"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:30Z"}