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Speech Synthesis Using Unsupervised ...

California State University, Long Beach.

Speech Synthesis Using Unsupervised Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Speech Synthesis Using Unsupervised Learning./
Author:	Datta, Aditi.
Description:	1 online resource (215 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-04.
Contained By:	Masters Abstracts International57-04(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355615241

Speech Synthesis Using Unsupervised Learning.
Datta, Aditi.

Speech Synthesis Using Unsupervised Learning. - 1 online resource (215 pages)

Source: Masters Abstracts International, Volume: 57-04.

Thesis (M.S.)

Includes bibliographical references

This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from a trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner and so enables the creation of systems using textual data that are not annotated. The method, therefore, aids the building of systems for languages in which conventional linguistic resources are scarce but is not restricted to these languages.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355615241Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Speech Synthesis Using Unsupervised Learning.
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020 $a 9780355615241
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035 $a AAI10639160
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099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Datta, Aditi. $3 1184038
245 1 0 $a Speech Synthesis Using Unsupervised Learning.
264 0 $c 2017
300 $a 1 online resource (215 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-04.
500 $a Adviser: Chit-Sang Tsang.
502 $a Thesis (M.S.) $c California State University, Long Beach $d 2017.
504 $a Includes bibliographical references
520 $a This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from a trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner and so enables the creation of systems using textual data that are not annotated. The method, therefore, aids the building of systems for languages in which conventional linguistic resources are scarce but is not restricted to these languages.
520 $a The distributional analysis proposed here places the textual objects analyzed in a continuous-valued space, rather than specifying a hard categorization of those objects. This space is then partitioned during the training of acoustic models for synthesis, so that the models generalize over objects' surface forms in a way that is acoustically relevant.
520 $a The method is applied to three levels of textual analysis: to the characterization of sub-syllabic units, word units, and utterances. The entire system was built with no reliance on manually labeled data or language-specific expertise. Results of a subjective evaluation are presented.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Language. $3 571568
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0679
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a California State University, Long Beach. $b Electrical Engineering. $3 1180451
773 0 $t Masters Abstracts International $g 57-04(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10639160 $z click for full text (PQDT)

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