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Automatic Assessment of Parkinsonian Speech [electronic resource] : First Workshop, AAPS 2019, Cambridge, Massachussets, USA, September 20–21, 2019, Revised Selected Papers /

Contributor(s): Material type: TextTextSeries: Communications in Computer and Information Science ; 1295Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020Description: IX, 125 p. 6 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783030656546
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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