國立虎尾科技大學 |

Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures./
作者:	Brizan, David Guy.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	165 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10976731
ISBN:	9780438732414

Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures.
Brizan, David Guy.

Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 165 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--City University of New York, 2019.

This item is not available from ProQuest Dissertations & Theses.

Spoken language understanding systems are error-prone for several reasons, including individual speech variability. This is manifested in many ways, among which are differences in pronunciation, lexical inventory, grammar and disfluencies. There is, however, a lot of evidence pointing to stable language usage within subgroups of a language population. We call these subgroups linguistic subcultures. The two broad problems are defined and a survey of the work in this space is performed. The two broad problems are: linguistic subculture detection, commonly performed via Language Identification, Accent Identification or Dialect Identification approaches; and speech and language processing tasks taken which may see increases in performance by modeling for each linguistic subculture. The data used in the experiments are drawn from four corpora: Accents of the British Isles (ABI), Intonational Variation in English (IViE), the NIST Language Recognition Evaluation Plan (LRE15) and Switchboard. The speakers in the corpora come from different parts of the United Kingdom and the United States and were provided different stimuli. From the speech samples, two features sets are used in the experiments. A number of experiments to determine linguistic subcultures are conducted. The set of experiments cover a number of approaches including the use traditional machine learning approaches shown to be effective for similar tasks in the past, each with multiple feature sets. State-of-the-art deep learning approaches are also applied to this problem. Two large automatic speech recognition (ASR) experiments are performed against all three corpora: one, "monolithic" experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. For the discourse markers labeled in the Switchboard corpus, there are some interesting trends when examined through the lens of the speakers in their linguistic subcultures. Two large dialogue acts experiments are performed against the labeled portion of the Switchboard corpus: one, "monocultural" (or "monolithic") experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. We conclude by discussing applications of this work, the changing landscape of natural language processing and suggestions for future research.

ISBN: 9780438732414Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Natural language processing

Culture Clubs Processing Speech by Deriving and Exploiting Linguistic Subcultures.
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520 $a Spoken language understanding systems are error-prone for several reasons, including individual speech variability. This is manifested in many ways, among which are differences in pronunciation, lexical inventory, grammar and disfluencies. There is, however, a lot of evidence pointing to stable language usage within subgroups of a language population. We call these subgroups linguistic subcultures. The two broad problems are defined and a survey of the work in this space is performed. The two broad problems are: linguistic subculture detection, commonly performed via Language Identification, Accent Identification or Dialect Identification approaches; and speech and language processing tasks taken which may see increases in performance by modeling for each linguistic subculture. The data used in the experiments are drawn from four corpora: Accents of the British Isles (ABI), Intonational Variation in English (IViE), the NIST Language Recognition Evaluation Plan (LRE15) and Switchboard. The speakers in the corpora come from different parts of the United Kingdom and the United States and were provided different stimuli. From the speech samples, two features sets are used in the experiments. A number of experiments to determine linguistic subcultures are conducted. The set of experiments cover a number of approaches including the use traditional machine learning approaches shown to be effective for similar tasks in the past, each with multiple feature sets. State-of-the-art deep learning approaches are also applied to this problem. Two large automatic speech recognition (ASR) experiments are performed against all three corpora: one, "monolithic" experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. For the discourse markers labeled in the Switchboard corpus, there are some interesting trends when examined through the lens of the speakers in their linguistic subcultures. Two large dialogue acts experiments are performed against the labeled portion of the Switchboard corpus: one, "monocultural" (or "monolithic") experiment for all the speakers in each corpus and another for the speakers in groups according to their identified linguistic subcultures. We conclude by discussing applications of this work, the changing landscape of natural language processing and suggestions for future research.
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