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Features and methods for automatic d...

Rojas, David Michael.

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Features and methods for automatic dialect identification.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Features and methods for automatic dialect identification./
Author:	Rojas, David Michael.
Description:	179 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-08, Section: A, page: 2867.
Contained By:	Dissertation Abstracts International71-08A.
Subject:	Language, Linguistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409783
ISBN:	9781124075129

Features and methods for automatic dialect identification.
Rojas, David Michael.

Features and methods for automatic dialect identification. - 179 p.

Source: Dissertation Abstracts International, Volume: 71-08, Section: A, page: 2867.

Thesis (Ph.D.)--Indiana University, 2010.

Systematic differences among regional U.S. English speech are recognizable to native speakers to varying degrees. This has been demonstrated by researchers in perceptual dialectology who ask listeners to match a speaker to his or her dialect region. Machines have also been able to identify the regional origin of a speaker to some degree, although attempts to this end have typically not been as successful as efforts to identify a speaker's language. Inspired by research in automatic language identification, musical artist classification, dialectometry, and pattern recognition more broadly, this thesis seeks to measure the degree to which a machine can accurately identify the region of origin of a speaker of U.S. English and to determine whether knowledge of speaker gender contributes positively to correct classification. We first examine the relationships between the distributional properties of phonemes and dialect, constructing a language model representing phone sequences to identify the dialect of a speaker. We next draw upon linguistic notions that vowels contribute more heavily than consonants to regional differences and that sub-phonemic acoustic variation and prosodic features provide information useful in automatic dialect identification (ADI). Our classification schemes are able to correctly label the dialect of a speaker at rates comparable to, if not slightly better than, naive native speaker listeners. To enrich our study, a dialectometric analysis is then conducted, again using phone sequences alone, from which high level dialect relationships emerge that reflect research findings from both perceptual dialectology as well as more traditional sociolinguistic methods. The dissertation contributes a substantiated methodology and proof of concept of an ADI framework that complements existing research in the areas of sociolinguistics and perceptual dialectology.

ISBN: 9781124075129Subjects--Topical Terms:

1018079
Language, Linguistics.

Features and methods for automatic dialect identification.
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035 $a (UMI)AAI3409783
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100 1 $a Rojas, David Michael. $3 1674910
245 1 0 $a Features and methods for automatic dialect identification.
300 $a 179 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-08, Section: A, page: 2867.
500 $a Advisers: Kenneth de Jong; John Paolillo.
502 $a Thesis (Ph.D.)--Indiana University, 2010.
520 $a Systematic differences among regional U.S. English speech are recognizable to native speakers to varying degrees. This has been demonstrated by researchers in perceptual dialectology who ask listeners to match a speaker to his or her dialect region. Machines have also been able to identify the regional origin of a speaker to some degree, although attempts to this end have typically not been as successful as efforts to identify a speaker's language. Inspired by research in automatic language identification, musical artist classification, dialectometry, and pattern recognition more broadly, this thesis seeks to measure the degree to which a machine can accurately identify the region of origin of a speaker of U.S. English and to determine whether knowledge of speaker gender contributes positively to correct classification. We first examine the relationships between the distributional properties of phonemes and dialect, constructing a language model representing phone sequences to identify the dialect of a speaker. We next draw upon linguistic notions that vowels contribute more heavily than consonants to regional differences and that sub-phonemic acoustic variation and prosodic features provide information useful in automatic dialect identification (ADI). Our classification schemes are able to correctly label the dialect of a speaker at rates comparable to, if not slightly better than, naive native speaker listeners. To enrich our study, a dialectometric analysis is then conducted, again using phone sequences alone, from which high level dialect relationships emerge that reflect research findings from both perceptual dialectology as well as more traditional sociolinguistic methods. The dissertation contributes a substantiated methodology and proof of concept of an ADI framework that complements existing research in the areas of sociolinguistics and perceptual dialectology.
590 $a School code: 0093.
650 4 $a Language, Linguistics. $3 1018079
650 4 $a Sociology, Sociolinguistics. $3 1669082
650 4 $a Artificial Intelligence. $3 769149
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690 $a 0636
690 $a 0800
710 2 $a Indiana University. $b Linguistics. $3 1674911
773 0 $t Dissertation Abstracts International $g 71-08A.
790 1 0 $a de Jong, Kenneth, $e advisor
790 1 0 $a Paolillo, John, $e advisor
790 1 0 $a Kubler, Sandra $e committee member
790 1 0 $a Gasser, Michael $e committee member
790 $a 0093
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409783