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Improving tone recognition with nucl...

Wang, Siwei.

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Improving tone recognition with nucleus modeling and sequential learning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improving tone recognition with nucleus modeling and sequential learning./
Author:	Wang, Siwei.
Description:	133 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6240.
Contained By:	Dissertation Abstracts International71-10B.
Subject:	Language, Linguistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3419706
ISBN:	9781124198194

Improving tone recognition with nucleus modeling and sequential learning.
Wang, Siwei.

Improving tone recognition with nucleus modeling and sequential learning. - 133 p.

Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6240.

Thesis (Ph.D.)--The University of Chicago, 2010.

Mandarin Chinese and many other tonal languages use tones that are defined as specific pitch patterns to distinguish syllables otherwise ambiguous. It had been shown that tones carry at least as much information as vowels in Mandarin Chinese [Surendran et al., 2005]. Surprisingly, though, many speech recognition systems for Mandarin Chinese have not performed explicit tone recognition. Systems that had initially tried to incorporate tone information by recognizing tonal syllables often found that errors at the tone-recognition stage outweighed any benefit from the percentage of correctly recognized tones. Instead, these systems recognized toneless syllables and used large-scale language models for disambiguation. We attribute these problems in early tone-recognition approaches to several limitations of their tone modeling strategies, which our approaches aim to correct.

ISBN: 9781124198194Subjects--Topical Terms:

1018079
Language, Linguistics.

Improving tone recognition with nucleus modeling and sequential learning.
LDR:04071nam 2200337 4500 001 1400488
005 20111010080605.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124198194
035 $a (UMI)AAI3419706
035 $a AAI3419706
040 $a UMI $c UMI
100 1 $a Wang, Siwei. $3 1679550
245 1 0 $a Improving tone recognition with nucleus modeling and sequential learning.
300 $a 133 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-10, Section: B, page: 6240.
500 $a Adviser: Gina-Anne Levow.
502 $a Thesis (Ph.D.)--The University of Chicago, 2010.
520 $a Mandarin Chinese and many other tonal languages use tones that are defined as specific pitch patterns to distinguish syllables otherwise ambiguous. It had been shown that tones carry at least as much information as vowels in Mandarin Chinese [Surendran et al., 2005]. Surprisingly, though, many speech recognition systems for Mandarin Chinese have not performed explicit tone recognition. Systems that had initially tried to incorporate tone information by recognizing tonal syllables often found that errors at the tone-recognition stage outweighed any benefit from the percentage of correctly recognized tones. Instead, these systems recognized toneless syllables and used large-scale language models for disambiguation. We attribute these problems in early tone-recognition approaches to several limitations of their tone modeling strategies, which our approaches aim to correct.
520 $a The first limitation we identify is that most early tone-recognition approaches did not model the effect of neighboring syllables in continuous speech, known as coarticulation, which often severely distorts the realizations of the underlying tones. We propose two nucleus modeling techniques to locate the most reliably produced portion of the syllable and remove the regions affected by coarticulation. The first method manipulates a geometric representation of both pitch and amplitude to locate the tone nucleus as the region with the greatest articulatory effort. The second method models tone nuclei based on the likely output of a landmark-based vowel detector, linking the sonority profile of the speech signal to the stability of tone production. Tone recognition using both nucleus modeling techniques outperforms tone recognition using either baseline nucleus approaches or the full syllable. Furthermore, the second methodthe landmark-based nucleus modelingshows a 15% improvement over two published tone-recognition frameworks.
520 $a The second limitation we identify is that most early tone-recognition approaches provided limited or no modeling of tone variation derived from phrase, sentence, and topic-level intonation. Most previous modeling techniques aimed at modifying the pitch levels and ranges of tones to adjust for the impact of these intonation events. However, we demonstrate that none of these approaches adequately compensates for the boundary effects of these intonational units. Our approach employs sequential learning frameworks, both to investigate their modeling on tone production with intonation events and to model the effect of the broader intonation context on tone realization and recognition. Our analyses also show that all three factorstone identity, level of intonation event, and structure of sequential graphical modelingcan influence recognition performance. This is because different tones show pitch patterns that vary across phrase, sentence, and story-level boundaries, and because the structures of a sequential graphical model make them encode these variations differently.
590 $a School code: 0330.
650 4 $a Language, Linguistics. $3 1018079
650 4 $a Computer Science. $3 626642
690 $a 0290
690 $a 0984
710 2 $a The University of Chicago. $b Computer Science. $3 1674744
773 0 $t Dissertation Abstracts International $g 71-10B.
790 1 0 $a Levow, Gina-Anne, $e advisor
790 1 0 $a Goldsmith, John $e committee member
790 1 0 $a Livescu, Karen $e committee member
790 1 0 $a Riggle, Jason $e committee member
790 $a 0330
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3419706