Automatic Assessment of Parkinsonian Speech: First Workshop, AAPS 2019, Cambridge, Massachussets, USA, September 20–21, 2019, Revised Selected Papers
Author:   Juan I. Godino-Llorente
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   1295
ISBN:  

9783030656539


Pages:   125
Publication Date:   03 January 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Automatic Assessment of Parkinsonian Speech: First Workshop, AAPS 2019, Cambridge, Massachussets, USA, September 20–21, 2019, Revised Selected Papers


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Overview

This book constitutes the revised and extended papers of the First Automatic Assessment of Parkinsonian Speech Workshop, AAPS 2019, held in Cambridge, Massachusetts, USA, in September 2019.  The 6 full papers were thoroughly reviewed and selected from 15 submissions. They present recent research on the automatic assessment of parkinsonian speech from the point of view of such disciplines as machine learning, speech technology, phonetics, neurology, and speech therapy

Full Product Details

Author:   Juan I. Godino-Llorente
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   1295
Weight:   0.454kg
ISBN:  

9783030656539


ISBN 10:   3030656535
Pages:   125
Publication Date:   03 January 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Acoustic Analysis and Voice Quality in Parkinson Disease.- Sources of Intraspeaker Variation in Parkinsonian Speech related to Speaking Style.- Review of the prosodic aspect of speech for the automatic detection and assessment of Parkinson's disease.- Automatic processing of aerodynamic parameters in parkinsonian dysarthria.- Approaches to evaluate parkinsonian speech using artificial models.- Predicting UPDRS scores in Parkinson's disease using voice signals: a deep learning/transfer-learning-based approach.

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