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Deep learning based speech quality prediction

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning based speech quality predictionby Gabriel Mittag.
作者:	Mittag, Gabriel.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xiv, 165 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Deep learning (Machine learning)
電子資源:	https://doi.org/10.1007/978-3-030-91479-0
ISBN:	9783030914790$q(electronic bk.)

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$q(electronic bk.)

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

913129
Deep learning (Machine learning)

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

Dewey Class. No.: 006.31

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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.
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