東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A Computational Model of the Relatio...

Jiao, Yishan.

Linked to FindBook

Google Book

Amazon

博客來

A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics./
Author:	Jiao, Yishan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	115 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-11, Section: B.
Contained By:	Dissertations Abstracts International80-11B.
Subject:	Communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13863625
ISBN:	9781392140840

A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics.
Jiao, Yishan.

A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 115 p.

Source: Dissertations Abstracts International, Volume: 80-11, Section: B.

Thesis (Ph.D.)--Arizona State University, 2019.

This item must not be sold to any third party vendors.

Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.

ISBN: 9781392140840Subjects--Topical Terms:

524709
Communication.

A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics.
LDR:03132nmm a2200337 4500 001 2207891
005 20190923114248.5
008 201008s2019 ||||||||||||||||| ||eng d
020 $a 9781392140840
035 $a (MiAaPQ)AAI13863625
035 $a (MiAaPQ)asu:18917
035 $a AAI13863625
040 $a MiAaPQ $c MiAaPQ
100 1 $a Jiao, Yishan. $3 3434889
245 1 0 $a A Computational Model of the Relationship Between Speech Intelligibility and Speech Acoustics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 115 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-11, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Berisha, Visar;Liss, Julie.
502 $a Thesis (Ph.D.)--Arizona State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.
590 $a School code: 0010.
650 4 $a Communication. $3 524709
650 4 $a Speech therapy. $3 520446
650 4 $a Electrical engineering. $3 649834
690 $a 0459
690 $a 0460
690 $a 0544
710 2 $a Arizona State University. $b Speech and Hearing Science. $3 2102134
773 0 $t Dissertations Abstracts International $g 80-11B.
790 $a 0010
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13863625