東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Automatic Question Detection From Pr...

Hirsch, Rachel Valia.

Linked to FindBook

Google Book

Amazon

博客來

Automatic Question Detection From Prosodic Speech Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automatic Question Detection From Prosodic Speech Analysis./
Author:	Hirsch, Rachel Valia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	47 p.
Notes:	Source: Masters Abstracts International, Volume: 81-04.
Contained By:	Masters Abstracts International81-04.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13904578
ISBN:	9781088329702

Automatic Question Detection From Prosodic Speech Analysis.
Hirsch, Rachel Valia.

Automatic Question Detection From Prosodic Speech Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 47 p.

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.S.)--Colorado State University, 2019.

This item must not be sold to any third party vendors.

Human-agent spoken communication has become ubiquitous over the last decade, with assistants such as Siri and Alexa being used more every day. An AI agent needs to understand exactly what the user says to it and respond accurately. To correctly respond, the agent has to know whether it is being given a command or asked a question. In Standard American English (SAE), both word choice and intonation of the speaker are necessary to discern the true sentiment of an utterance. Much Natural Language Processing (NLP) research has been done into automatically determining these sentence types using word choice alone. However, intonation is ultimately the key to understanding the sentiment of a spoken sentence. This thesis uses a series of attributes to characterize vocal prosody of utterances to train classifiers to detect questions. The dataset used to train these classifiers is a series of hearings by the Supreme Court of the United States (SCOTUS). Prosody-trained classifier results are compared against a text-based classifier, using Google Speech-to-Text transcriptions of the same dataset.

ISBN: 9781088329702Subjects--Topical Terms:

523869
Computer science.

Automatic Question Detection From Prosodic Speech Analysis.
LDR:02051nmm a2200301 4500 001 2264300
005 20200423112950.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781088329702
035 $a (MiAaPQ)AAI13904578
035 $a AAI13904578
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hirsch, Rachel Valia. $3 3541415
245 1 0 $a Automatic Question Detection From Prosodic Speech Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 47 p.
500 $a Source: Masters Abstracts International, Volume: 81-04.
500 $a Advisor: Draper, Bruce;Whitley, Darrell.
502 $a Thesis (M.S.)--Colorado State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Human-agent spoken communication has become ubiquitous over the last decade, with assistants such as Siri and Alexa being used more every day. An AI agent needs to understand exactly what the user says to it and respond accurately. To correctly respond, the agent has to know whether it is being given a command or asked a question. In Standard American English (SAE), both word choice and intonation of the speaker are necessary to discern the true sentiment of an utterance. Much Natural Language Processing (NLP) research has been done into automatically determining these sentence types using word choice alone. However, intonation is ultimately the key to understanding the sentiment of a spoken sentence. This thesis uses a series of attributes to characterize vocal prosody of utterances to train classifiers to detect questions. The dataset used to train these classifiers is a series of hearings by the Supreme Court of the United States (SCOTUS). Prosody-trained classifier results are compared against a text-based classifier, using Google Speech-to-Text transcriptions of the same dataset.
590 $a School code: 0053.
650 4 $a Computer science. $3 523869
650 4 $a Linguistics. $3 524476
650 4 $a Datasets. $3 3541416
650 4 $a Speech. $3 530580
690 $a 0984
690 $a 0290
710 2 $a Colorado State University. $b Computer Science. $3 1672484
773 0 $t Masters Abstracts International $g 81-04.
790 $a 0053
791 $a M.S.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13904578