{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/37104"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/37104","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Classification of vocal fold vibration as regular or irregular in normal, voiced speech","abstract":"Irregular phonation serves an important communicative function in human speech and occurs allophonically in American English. This thesis uses cues from both the temporal and frequency domains - such as fundamental frequency, normalized RMS amplitude, smoothed-energy-difference amplitude (a measure of abruptness in energy variations) and shift-difference amplitude (a measures of periodicity) -to classify segments of regular and irregular phonation in normal, continuous speech. Support Vector Machines (SVMs) are used to classify the tokens as examples of either regular or irregular phonation. The tokens are extracted from the TIMIT database, and are extracted from 151 different speakers. Both genders are well represented, and the tokens occur in various contexts within the utterance. The train-set uses 114 different speakers, while the test-set uses another 37 speakers. A total of 292 of 320 irregular tokens (recognition rate of 91.25% with a false alarm rate of 4.98%), and 4105 of 4320 regular tokens (recognition rate of 95.02% with a false alarm rate of 8.75%) are correctly identified.","abstract_html":"Irregular phonation serves an important communicative function in human speech and occurs allophonically in American English. This thesis uses cues from both the temporal and frequency domains - such as fundamental frequency, normalized RMS amplitude, smoothed-energy-difference amplitude (a measure of abruptness in energy variations) and shift-difference amplitude (a measures of periodicity) -to classify segments of regular and irregular phonation in normal, continuous speech. Support Vector Machines (SVMs) are used to classify the tokens as examples of either regular or irregular phonation. The tokens are extracted from the TIMIT database, and are extracted from 151 different speakers. Both genders are well represented, and the tokens occur in various contexts within the utterance. The train-set uses 114 different speakers, while the test-set uses another 37 speakers. A total of 292 of 320 irregular tokens (recognition rate of 91.25% with a false alarm rate of 4.98%), and 4105 of 4320 regular tokens (recognition rate of 95.02% with a false alarm rate of 8.75%) are correctly identified.","abstract_has_math":false,"creators":["Surana, Kushan Krishna"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Janet Slifka."],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006","date_published":"2006","updated_at":"2026-07-22T22:21:06Z","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/1721.1/37104","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Janet Slifka."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Surana, Kushan Krishna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2007-04-03T17:11:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2007-04-03T17:11:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2006"]},{"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/1721.1/37104"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.","Includes bibliographical references (p. 91-97)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Irregular phonation serves an important communicative function in human speech and occurs allophonically in American English. This thesis uses cues from both the temporal and frequency domains - such as fundamental frequency, normalized RMS amplitude, smoothed-energy-difference amplitude (a measure of abruptness in energy variations) and shift-difference amplitude (a measures of periodicity) -to classify segments of regular and irregular phonation in normal, continuous speech. Support Vector Machines (SVMs) are used to classify the tokens as examples of either regular or irregular phonation. The tokens are extracted from the TIMIT database, and are extracted from 151 different speakers. Both genders are well represented, and the tokens occur in various contexts within the utterance. The train-set uses 114 different speakers, while the test-set uses another 37 speakers. A total of 292 of 320 irregular tokens (recognition rate of 91.25% with a false alarm rate of 4.98%), and 4105 of 4320 regular tokens (recognition rate of 95.02% with a false alarm rate of 8.75%) are correctly identified.","(cont.) The high recognition rates are an indicator that the set of acoustic cues are robust in accurately identifying a token as regular or irregular, even in cases where one or two acoustic cues show unexpected values. Also, analysis of irregular tokens in the training set (1331 irregular tokens) shows that 78% occur at word boundaries and 5% occur at syllable boundaries. Of the irregular tokens at syllable boundaries, 72% are either at the junction of a compound-word (e.g \"outcast;\") or at the junction of a base word and a suffix. Of the irregular tokens which do not occur at word or syllable boundaries, 70% occur adjacent to voiceless consonants mostly in utterance-final location. These observations support irregular phonation as a cue for syntactic boundaries in connected speech, and combined with the robust classification results to separate regular phonation from irregular phonation, could be used to improve speech recognition and lexical access models."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Classification of vocal fold vibration as regular or irregular in normal, voiced speech"]}]}],"canonical_facts":{"dc:contributor.advisor":["Janet Slifka."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Surana, Kushan Krishna"],"dc:date.accessioned":["2007-04-03T17:11:57Z"],"dc:date.available":["2007-04-03T17:11:57Z"],"dc:date.issued":["2006"],"dc:description":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.","Includes bibliographical references (p. 91-97)."],"dc:description.abstract":["Irregular phonation serves an important communicative function in human speech and occurs allophonically in American English. This thesis uses cues from both the temporal and frequency domains - such as fundamental frequency, normalized RMS amplitude, smoothed-energy-difference amplitude (a measure of abruptness in energy variations) and shift-difference amplitude (a measures of periodicity) -to classify segments of regular and irregular phonation in normal, continuous speech. Support Vector Machines (SVMs) are used to classify the tokens as examples of either regular or irregular phonation. The tokens are extracted from the TIMIT database, and are extracted from 151 different speakers. Both genders are well represented, and the tokens occur in various contexts within the utterance. The train-set uses 114 different speakers, while the test-set uses another 37 speakers. A total of 292 of 320 irregular tokens (recognition rate of 91.25% with a false alarm rate of 4.98%), and 4105 of 4320 regular tokens (recognition rate of 95.02% with a false alarm rate of 8.75%) are correctly identified.","(cont.) The high recognition rates are an indicator that the set of acoustic cues are robust in accurately identifying a token as regular or irregular, even in cases where one or two acoustic cues show unexpected values. Also, analysis of irregular tokens in the training set (1331 irregular tokens) shows that 78% occur at word boundaries and 5% occur at syllable boundaries. Of the irregular tokens at syllable boundaries, 72% are either at the junction of a compound-word (e.g \"outcast;\") or at the junction of a base word and a suffix. Of the irregular tokens which do not occur at word or syllable boundaries, 70% occur adjacent to voiceless consonants mostly in utterance-final location. These observations support irregular phonation as a cue for syntactic boundaries in connected speech, and combined with the robust classification results to separate regular phonation from irregular phonation, could be used to improve speech recognition and lexical access models."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/37104"],"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":["Classification of vocal fold vibration as regular or irregular in normal, voiced speech"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:06Z"}