東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data-Driven Non-Intrusive Speech Qua...

Dong, Xuan,

FindBook

Google Book

Amazon

博客來

Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment // Xuan Dong.
作者:	Dong, Xuan,
面頁冊數:	1 electronic resource (193 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315114
ISBN:	9798569915880

Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment /
Dong, Xuan,

Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment /Xuan Dong. - 1 electronic resource (193 pages)

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Speech quality and intelligibility are vital factors when assessing a listening environment, communication channel, or speech enhancement algorithm. Subjective listening studies are the most accurate forms of assessing speech quality and intelligibility. However, this assessment form is generally costly and time-consuming to perform when a large-scale assessment is needed. Thus, objective methods are often used since they provide low cost and efficient speech assessment quickly.Objective methods can be divided into two categories. Intrusive measures assess a distorted speech signal's quality and intelligibility by comparing it to its clean, undistorted version. Hence, a clean reference signal is required. A fundamental limitation of intrusive measures is that the reference signal is usually not available in real-world environments or challenging to obtain. Non-intrusive measures, on the other hand, assess speech based on the distorted signal only, which means that real-world assessment is possible. Although existing non-intrusive approaches enable real-world evaluation, their real-world capabilities are limited since (1) current measures are developed from simulated data that does not adequately model real environments; and (2) they predict objective scores that are not always strongly correlated with subjective ratings as compared to their intrusive counterparts. Hence, an active research area involves developing non-intrusive algorithms that can assess speech in real-life scenarios and are strongly correlated with human assessment.This dissertation developed several data-driven non-intrusive algorithms to better assess speech quality and intelligibility in real environments. The contribution of this dissertation is threefold. First, a two-stage framework that estimates the objective score of these intrusive measures at the frame-level is proposed. Second, three neural network-based models are developed to provide utterance-level speech evaluation. Third, since a large dataset of real-world signals with listener quality ratings did not previously exist, to help facilitate real-world assessment, crowdsourcing listening studies are conducted to obtain perceptual quality ratings from human participants. An encoder-decoder model with an attention mechanism is proposed to predict the human-level perceived speech quality on real-world signals.

English

ISBN: 9798569915880Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Speech quality

Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment /
LDR:03869nmm a22004573i 4500 001 2432021
005 20250626110544.5
006 m o d
007 cr|nu||||||||
008 261011s2021 miu||||||m |||||||eng d
020 $a 9798569915880
035 $a (MiAaPQD)AAI28315114
035 $a AAI28315114
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Dong, Xuan, $e author. $3 3821098
245 1 0 $a Data-Driven Non-Intrusive Speech Quality and Intelligibility Assessment / $c Xuan Dong.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 1 electronic resource (193 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
500 $a Advisors: Williamson, Donald S. Committee members: Leake, David B.; Crandall, David J.; Kim, Minje.
502 $b Ph.D. $c Indiana University $d 2021.
520 $a Speech quality and intelligibility are vital factors when assessing a listening environment, communication channel, or speech enhancement algorithm. Subjective listening studies are the most accurate forms of assessing speech quality and intelligibility. However, this assessment form is generally costly and time-consuming to perform when a large-scale assessment is needed. Thus, objective methods are often used since they provide low cost and efficient speech assessment quickly.Objective methods can be divided into two categories. Intrusive measures assess a distorted speech signal's quality and intelligibility by comparing it to its clean, undistorted version. Hence, a clean reference signal is required. A fundamental limitation of intrusive measures is that the reference signal is usually not available in real-world environments or challenging to obtain. Non-intrusive measures, on the other hand, assess speech based on the distorted signal only, which means that real-world assessment is possible. Although existing non-intrusive approaches enable real-world evaluation, their real-world capabilities are limited since (1) current measures are developed from simulated data that does not adequately model real environments; and (2) they predict objective scores that are not always strongly correlated with subjective ratings as compared to their intrusive counterparts. Hence, an active research area involves developing non-intrusive algorithms that can assess speech in real-life scenarios and are strongly correlated with human assessment.This dissertation developed several data-driven non-intrusive algorithms to better assess speech quality and intelligibility in real environments. The contribution of this dissertation is threefold. First, a two-stage framework that estimates the objective score of these intrusive measures at the frame-level is proposed. Second, three neural network-based models are developed to provide utterance-level speech evaluation. Third, since a large dataset of real-world signals with listener quality ratings did not previously exist, to help facilitate real-world assessment, crowdsourcing listening studies are conducted to obtain perceptual quality ratings from human participants. An encoder-decoder model with an attention mechanism is proposed to predict the human-level perceived speech quality on real-world signals.
546 $a English
590 $a School code: 0093
650 4 $a Computer science. $3 523869
650 4 $a Speech therapy. $3 520446
650 4 $a Technical communication. $3 3172863
650 4 $a Audiology. $3 537237
650 4 $a Information technology. $3 532993
653 $a Speech quality
653 $a Intelligibility assessment
653 $a Crowdsourcing listening studies
653 $a Encoder
653 $a Decoder
690 $a 0984
690 $a 0489
690 $a 0643
690 $a 0300
690 $a 0460
710 2 $a Indiana University. $b Computer Science. $e degree granting institution. $3 3816423
720 1 $a Williamson, Donald S. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 82-08B.
790 $a 0093
791 $a Ph.D.
792 $a 2021
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28315114