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Automatic V/UV/S classification of continuous speech without a predetermined training set

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Date Issued:
1989
Summary:
In speech analysis, a Voiced-Unvoiced-Silence (V/UV/S) decision is performed through pattern recognition, based on measurements made on the signal. The examined speech segment is assigned to a particular class, V/UV/S, based on a minimum probability-of-error decision rule which is obtained under the assumption that the measured parameters are distributed according to a multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. If the recording conditions vary considerably, a new set of training data is required. With the assumption that all three classes exist in the incoming speech signal, this research describes an automatic parametric learning method. Such a method estimates the means and covariances from the incoming speech signal and provides a reliable classification in any reasonable acoustic environment. This approach eliminates the necessity for the manual classification of training data and has the capability of being self-adapting to the background acoustic environment as well as to speech level variations. Thus the presented approach can be readily applied to on-line continuous speech classification without prior recognition.
Title: Automatic V/UV/S classification of continuous speech without a predetermined training set.
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Name(s): Leung, Chung Sing.
Florida Atlantic University, Degree grantor
Kostopoulos, George, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1989
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 212 p.
Language(s): English
Summary: In speech analysis, a Voiced-Unvoiced-Silence (V/UV/S) decision is performed through pattern recognition, based on measurements made on the signal. The examined speech segment is assigned to a particular class, V/UV/S, based on a minimum probability-of-error decision rule which is obtained under the assumption that the measured parameters are distributed according to a multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. If the recording conditions vary considerably, a new set of training data is required. With the assumption that all three classes exist in the incoming speech signal, this research describes an automatic parametric learning method. Such a method estimates the means and covariances from the incoming speech signal and provides a reliable classification in any reasonable acoustic environment. This approach eliminates the necessity for the manual classification of training data and has the capability of being self-adapting to the background acoustic environment as well as to speech level variations. Thus the presented approach can be readily applied to on-line continuous speech classification without prior recognition.
Identifier: 12246 (digitool), FADT12246 (IID), fau:9152 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1989.
Subject(s): Speech synthesis
Speech processing systems
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12246
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.