{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24283"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24283","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computational differences between whispered and non-whispered speech","abstract":"Whispering is a common type of speech which is not often studied in speech technology. Perceptual and physiological studies show us that whispered speech is subtly di erent from phonated speech, and is surprisingly able to carry a tremendous amount of information. In this dissertation we con- sider the question: What makes whispering a good form of communication? We examine the di erences between normal phonated speech and whispered speech, and gauge the e ectiveness of state-of-the-art speech recognition al- gorithms at recognizing whisper. Our perceptual experiments add to the literature on the intelligibility of whispered speech. Comparisons with ASR results yield interesting insights into the di erences between the two systems. A method for building speech recognizers for whispered speech using lim- ited whispered speech data is proposed and evaluated. Our approach ef- fectively performs speaker-adaptation for whispered speech acoustic models without needing whispered speech from the target speaker. Results show im- provement over the standard speaker-independent models. Our work opens up additional avenues for research, which are outlined in the conclusions.","abstract_html":"Whispering is a common type of speech which is not often studied in speech technology. Perceptual and physiological studies show us that whispered speech is subtly di erent from phonated speech, and is surprisingly able to carry a tremendous amount of information. In this dissertation we con- sider the question: What makes whispering a good form of communication? We examine the di erences between normal phonated speech and whispered speech, and gauge the e ectiveness of state-of-the-art speech recognition al- gorithms at recognizing whisper. Our perceptual experiments add to the literature on the intelligibility of whispered speech. Comparisons with ASR results yield interesting insights into the di erences between the two systems. A method for building speech recognizers for whispered speech using lim- ited whispered speech data is proposed and evaluated. Our approach ef- fectively performs speaker-adaptation for whispered speech acoustic models without needing whispered speech from the target speaker. Results show im- provement over the standard speaker-independent models. Our work opens up additional avenues for research, which are outlined in the conclusions.","abstract_has_math":false,"creators":["Lim, Boon Pang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hasegawa-Johnson, Mark A.","Sproat, Richard W.","Shosted, Ryan K.","Levinson, Stephen E.","Lumetta, Steven S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:52:30Z","date_published":"2011-05-25T14:52:30Z","updated_at":"2026-07-22T22:25:23Z","subjects":["speech recognition","whispered speech","modified rhyme test","eigenvoices","speaker adaptation"],"languages":["en"],"rights":["(c) 2011 Boon Pang Lim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24283","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hasegawa-Johnson, Mark A.","Sproat, Richard W.","Shosted, Ryan K.","Levinson, Stephen E.","Lumetta, Steven S."]},{"key":"dc:creator","label":"Author","values":["Lim, Boon Pang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T14:52:30Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["speech recognition","whispered speech","modified rhyme test","eigenvoices","speaker adaptation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["(c) 2011 Boon Pang Lim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24283"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Whispering is a common type of speech which is not often studied in speech technology. Perceptual and physiological studies show us that whispered speech is subtly di erent from phonated speech, and is surprisingly able to carry a tremendous amount of information. In this dissertation we con- sider the question: What makes whispering a good form of communication? We examine the di erences between normal phonated speech and whispered speech, and gauge the e ectiveness of state-of-the-art speech recognition al- gorithms at recognizing whisper. Our perceptual experiments add to the literature on the intelligibility of whispered speech. Comparisons with ASR results yield interesting insights into the di erences between the two systems. A method for building speech recognizers for whispered speech using lim- ited whispered speech data is proposed and evaluated. Our approach ef- fectively performs speaker-adaptation for whispered speech acoustic models without needing whispered speech from the target speaker. Results show im- provement over the standard speaker-independent models. Our work opens up additional avenues for research, which are outlined in the conclusions.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-02-01T15:03:22Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 bplim_thesis_tex.tar.bz2: 113271 bytes, checksum: 8822ec3566330d2ce3a1dc4987c8cb4d (MD5) Lim_BoonPang.pdf: 3797403 bytes, checksum: 6b1379584dbbf0eb7596fd094861db0b (MD5)","Made available in DSpace on 2011-05-25T14:52:30Z (GMT). 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In this dissertation we con- sider the question: What makes whispering a good form of communication? We examine the di erences between normal phonated speech and whispered speech, and gauge the e ectiveness of state-of-the-art speech recognition al- gorithms at recognizing whisper. Our perceptual experiments add to the literature on the intelligibility of whispered speech. Comparisons with ASR results yield interesting insights into the di erences between the two systems. A method for building speech recognizers for whispered speech using lim- ited whispered speech data is proposed and evaluated. Our approach ef- fectively performs speaker-adaptation for whispered speech acoustic models without needing whispered speech from the target speaker. Results show im- provement over the standard speaker-independent models. Our work opens up additional avenues for research, which are outlined in the conclusions.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-02-01T15:03:22Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 bplim_thesis_tex.tar.bz2: 113271 bytes, checksum: 8822ec3566330d2ce3a1dc4987c8cb4d (MD5) Lim_BoonPang.pdf: 3797403 bytes, checksum: 6b1379584dbbf0eb7596fd094861db0b (MD5)","Made available in DSpace on 2011-05-25T14:52:30Z (GMT). No. of bitstreams: 3 Lim_BoonPang.pdf: 3797403 bytes, checksum: 6b1379584dbbf0eb7596fd094861db0b (MD5) license.txt: 4057 bytes, checksum: 170e0953c85a51530b3c517dcf36282d (MD5) bplim_thesis_tex.tar.bz2: 113271 bytes, checksum: 8822ec3566330d2ce3a1dc4987c8cb4d (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/24283"],"dc:language":["en"],"dc:rights":["(c) 2011 Boon Pang Lim"],"dc:subject":["speech recognition","whispered speech","modified rhyme test","eigenvoices","speaker adaptation"],"dc:title":["Computational differences between whispered and non-whispered speech"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:23Z"}