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Using Gaussian mixture model and partial least squares regression classifiers for robust speaker verification with various enhancement methods.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Using Gaussian mixture model and partial least squares regression classifiers for robust speaker verification with various enhancement methods./
作者:	Edwards, Joshua Scott.
面頁冊數:	1 online resource (155 pages)
附註:	Source: Masters Abstracts International, Volume: 56-03.
Contained By:	Masters Abstracts International56-03(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369658941

Using Gaussian mixture model and partial least squares regression classifiers for robust speaker verification with various enhancement methods.
Edwards, Joshua Scott.

Using Gaussian mixture model and partial least squares regression classifiers for robust speaker verification with various enhancement methods. - 1 online resource (155 pages)

Source: Masters Abstracts International, Volume: 56-03.

Thesis (M.S.)--Rowan University, 2017.

Includes bibliographical references

In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This thesis proposes the use of two parallel classifiers with several enhancement methods in order to improve the performance of the speaker verification system when noisy speech signals are used for authentication. Both classifiers are shown to receive statistically significant performance gains when signal-to-noise ratio estimation, affine transforms, and score-level fusion of features are all applied. These enhancement methods are validated in a large range of test conditions, from perfectly clean speech all the way down to speech where the noise is equally as loud as the speaker. After each classifier has been tuned to their best configuration, they are also fused together in different ways. In the end, the performances of the two classifiers are compared to each other and to the performances of their fusions. The fusion method where the scores of the classifiers are added together is found to be the best method.

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

Mode of access: World Wide Web

ISBN: 9781369658941Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Using Gaussian mixture model and partial least squares regression classifiers for robust speaker verification with various enhancement methods.
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500 $a Source: Masters Abstracts International, Volume: 56-03.
500 $a Adviser: Ravi P. Ramachandran.
502 $a Thesis (M.S.)--Rowan University, 2017.
504 $a Includes bibliographical references
520 $a In the presence of environmental noise, speaker verification systems inevitably see a decrease in performance. This thesis proposes the use of two parallel classifiers with several enhancement methods in order to improve the performance of the speaker verification system when noisy speech signals are used for authentication. Both classifiers are shown to receive statistically significant performance gains when signal-to-noise ratio estimation, affine transforms, and score-level fusion of features are all applied. These enhancement methods are validated in a large range of test conditions, from perfectly clean speech all the way down to speech where the noise is equally as loud as the speaker. After each classifier has been tuned to their best configuration, they are also fused together in different ways. In the end, the performances of the two classifiers are compared to each other and to the performances of their fusions. The fusion method where the scores of the classifiers are added together is found to be the best method.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rowan University. $b Electrical and Computer Engineering. $3 1194155
773 0 $t Masters Abstracts International $g 56-03(E).
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