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Approximate Bayesian Inference for R...

Drexel University.

Approximate Bayesian Inference for Robust Speech Processing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Approximate Bayesian Inference for Robust Speech Processing./
Author:	Maina, Ciira wa.
Description:	129 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: 6942.
Contained By:	Dissertation Abstracts International72-11B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3475796
ISBN:	9781124880631

Approximate Bayesian Inference for Robust Speech Processing.
Maina, Ciira wa.

Approximate Bayesian Inference for Robust Speech Processing. - 129 p.

Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: 6942.

Thesis (Ph.D.)--Drexel University, 2011.

Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. These parameters must often be estimated from noisy observations since speech signals are rarely obtained in 'clean' acoustic environments in the real world. As a result, the parameter estimation algorithms we employ must be robust to environmental factors such as additive noise and reverberation. In this work we derive and evaluate approximate Bayesian algorithms for the following speech processing tasks: 1) speech enhancement 2) speaker identification 3) speaker verification and 4) voice activity detection.

ISBN: 9781124880631Subjects--Topical Terms:

845382
Engineering, Electronics and Electrical.

Approximate Bayesian Inference for Robust Speech Processing.
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020 $a 9781124880631
035 $a (UMI)AAI3475796
035 $a AAI3475796
040 $a UMI $c UMI
100 1 $a Maina, Ciira wa. $3 845454
245 1 0 $a Approximate Bayesian Inference for Robust Speech Processing.
300 $a 129 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: 6942.
500 $a Adviser: John MacLaren Walsh.
502 $a Thesis (Ph.D.)--Drexel University, 2011.
520 $a Speech processing applications such as speech enhancement and speaker identification rely on the estimation of relevant parameters from the speech signal. These parameters must often be estimated from noisy observations since speech signals are rarely obtained in 'clean' acoustic environments in the real world. As a result, the parameter estimation algorithms we employ must be robust to environmental factors such as additive noise and reverberation. In this work we derive and evaluate approximate Bayesian algorithms for the following speech processing tasks: 1) speech enhancement 2) speaker identification 3) speaker verification and 4) voice activity detection.
520 $a Building on previous work in the field of statistical model based speech enhancement, we derive speech enhancement algorithms that rely on speaker dependent priors over linear prediction parameters. These speaker dependent priors allow us to handle speech enhancement and speaker identification in a joint framework. Furthermore, we show how these priors allow voice activity detection to be performed in a robust manner.
520 $a We also develop algorithms in the log spectral domain with applications in robust speaker verification. The use of speaker dependent priors in the log spectral domain is shown to improve equal error rates in noisy environments and to compensate for mismatch between training and testing conditions.
590 $a School code: 0065.
650 4 $a Engineering, Electronics and Electrical. $3 845382
690 $a 0544
710 2 $a Drexel University. $3 845455
773 0 $t Dissertation Abstracts International $g 72-11B.
790 1 0 $a Walsh, John MacLaren, $e advisor
790 $a 0065
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3475796

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