A pattern recognition approach to compare natural and synthetic speech

dc.contributor.advisorWaldron, Manjula B.
dc.contributor.committeeMemberMcInnis, Bayliss C.
dc.contributor.committeeMemberSinkhorn, Richard D.
dc.creatorSheshadri, S.
dc.date.accessioned2022-12-20T20:39:52Z
dc.date.available2022-12-20T20:39:52Z
dc.date.issued1978
dc.description.abstractIn this thesis an effort is made to determine the acoustic feature differences between natural and synthesized speech. Sentences spoken in a natural adult male voice and synthesized on VOTRAX ML-1 speech synthesizer were recorded in a sound proof booth. The recorded sentences were classified into voiced, unvoiced and silence regions contained in them. Parameters like the zerocrossing, linear prediction coefficient and energy were used in making this classification. The results obtained indicate that the synthesized speech tends to contain more unvoicing than the natural speech. The classification accuracy was 99% in the natural speech and 85% in the synthesized speech.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginreformatted digital
dc.format.mimetypeapplication/pdf
dc.identifier.other4844087
dc.identifier.urihttps://hdl.handle.net/10657/13093
dc.language.isoen
dc.rightsThis item is protected by copyright but is made available here under a claim of fair use (17 U.S.C. Section 107) for non-profit research and educational purposes. Users of this work assume the responsibility for determining copyright status prior to reusing, publishing, or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires express permission of the copyright holder.
dc.titleA pattern recognition approach to compare natural and synthetic speech
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Sheshadri_1978_4844087.pdf
Size:
4.9 MB
Format:
Adobe Portable Document Format