國立虎尾科技大學 |

Cross-Linguistic Acoustic Characteristics of Phonation : = A Machine Learning Approach.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Cross-Linguistic Acoustic Characteristics of Phonation :/
其他題名:	A Machine Learning Approach.
作者:	Panfili, Laura Maggia.
面頁冊數:	1 online resource (360 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A.
Contained By:	Dissertation Abstracts International79-12A(E).
標題:	Linguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780438175389

Cross-Linguistic Acoustic Characteristics of Phonation : = A Machine Learning Approach.
Panfili, Laura Maggia.

Cross-Linguistic Acoustic Characteristics of Phonation :A Machine Learning Approach. - 1 online resource (360 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A.

Thesis (Ph.D.)--University of Washington, 2018.

Includes bibliographical references

Phonation, the process of producing a quasi-periodic sound wave through vocal fold vibration, plays different roles in different languages. Phonation types, or voice qualities, are produced by adjusting the length, thickness, and separation of the vocal folds. In addition to being a complex physiological phenomenon, phonation is also a complex acoustic phenomenon; different phonation types are generally distinguished by a constellation of acoustic properties, and those properties vary from language to language. This dissertation presents a machine learning approach to investigating the role those acoustic properties play in phonation in different languages.

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

Mode of access: World Wide Web

ISBN: 9780438175389Subjects--Topical Terms:

557829
Linguistics.
Index Terms--Genre/Form:

554714
Electronic books.

Cross-Linguistic Acoustic Characteristics of Phonation : = A Machine Learning Approach.
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A.
500 $a Adviser: Richard Wright.
502 $a Thesis (Ph.D.)--University of Washington, 2018.
504 $a Includes bibliographical references
520 $a Phonation, the process of producing a quasi-periodic sound wave through vocal fold vibration, plays different roles in different languages. Phonation types, or voice qualities, are produced by adjusting the length, thickness, and separation of the vocal folds. In addition to being a complex physiological phenomenon, phonation is also a complex acoustic phenomenon; different phonation types are generally distinguished by a constellation of acoustic properties, and those properties vary from language to language. This dissertation presents a machine learning approach to investigating the role those acoustic properties play in phonation in different languages.
520 $a This study examines phonation in six languages from four families: English, Gujarati, Hmong, Mandarin, Mazatec, and Zapotec. These languages use phonation in a variety of ways, including contrastively, alongside tones, sociolinguistically, allophonically, and prosodically. Two machine learning algorithms---a Support Vector Machine and a Random Forest---are used to explore which acoustic properties best distinguish phonation types on vowels in each of those six languages. In addition to SVM weights and Random Forest importance, correlations and ablation studies are used in the analysis. Results reveal that while each of the six languages relies on a different subset of acoustic features to distinguish its phonation types, some features are consistently important. Phonation varies enough from language to language that languages should be treated separately for the study of phonation. However, all six languages rely on at least one variant of Harmonics-to-Noise Ratio, as well as Variance of Pitch Tracks, a new measure that takes advantage of the pitch tracking errors commonly found in non-modal phonation.
520 $a Machine learning was also used to fine tune a classifier for English phonation types. Unlike other voice quality classifiers, this study focuses on just English and on the three-way breathy vs. modal vs. creaky contrast, rather than on a binary creaky vs. non-creaky distinction. The best performing classifier developed here achieves a weighted F1 score of 0.864, which is on par with state-of-the-art phonation classifiers but performs a more complex task. However, it still struggles with breathy voicing, largely a consequence of data sparsity.
520 $a This dissertation demonstrates that machine learning is a powerful tool for the study of phonation. It illuminates some of the previously unexamined similarities and differences between phonation types in different languages, and introduces a new measure, Variance of Pitch Tracks, which proves quite useful in machine classification of phonation. In addition to contributing to the understanding of phonation, this dissertation presents a new methodology for its study.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Linguistics. $3 557829
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Washington. $b Linguistics. $3 1184011
773 0 $t Dissertation Abstracts International $g 79-12A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10827202 $z click for full text (PQDT)