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Deep learning based speech quality prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning based speech quality prediction/ by Gabriel Mittag.
Author:	Mittag, Gabriel.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xiv, 165 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA - A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-030-91479-0
ISBN:	9783030914790

Deep learning based speech quality prediction
Mittag, Gabriel.

Deep learning based speech quality prediction[electronic resource] /by Gabriel Mittag. - Cham :Springer International Publishing :2022. - xiv, 165 p. :ill., digital ;24 cm. - T-labs series in telecommunication services,2192-2829. - T-labs series in telecommunication services..

1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA - A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.

This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.

ISBN: 9783030914790

Standard No.: 10.1007/978-3-030-91479-0doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .M57 2022

Dewey Class. No.: 006.31

Deep learning based speech quality prediction
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245 1 0 $a Deep learning based speech quality prediction $h [electronic resource] / $c by Gabriel Mittag.
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300 $a xiv, 165 p. : $b ill., digital ; $c 24 cm.
490 1 $a T-labs series in telecommunication services, $x 2192-2829
505 0 $a 1. Introduction -- 2. Quality Assessment of Transmitted Speech -- 3. Neural Network Architectures for Speech Quality Prediction -- 4. Double-Ended Speech Quality Prediction Using Siamese Networks -- 5. Prediction of Speech Quality Dimensions With Multi-Task Learning -- 6. Bias-Aware Loss for Training From Multiple Datasets -- 7. NISQA - A Single-Ended Speech Quality Model -- 8. Conclusions -- A. Dataset Condition Tables -- B. Train and Validation Dataset Dimension Histograms -- References.
520 $a This book presents how to apply recent machine learning (deep learning) methods for the task of speech quality prediction. The author shows how recent advancements in machine learning can be leveraged for the task of speech quality prediction and provides an in-depth analysis of the suitability of different deep learning architectures for this task. The author then shows how the resulting model outperforms traditional speech quality models and provides additional information about the cause of a quality impairment through the prediction of the speech quality dimensions of noisiness, coloration, discontinuity, and loudness.
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a Speech processing systems. $3 590570
650 1 4 $a Digital and Analog Signal Processing. $3 3538815
650 2 4 $a User Interfaces and Human Computer Interaction. $3 892554
650 2 4 $a Natural Language Processing (NLP) $3 3381674
650 2 4 $a Engineering Acoustics. $3 1533220
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856 4 0 $u https://doi.org/10.1007/978-3-030-91479-0
950 $a Engineering (SpringerNature-11647)