Arias Vergara T (2022)
Publication Language: English
Publication Type: Authored book, Monography
Publication year: 2022
Publisher: Logos Verlag Berlin GmbH
Series: Studien zur Mustererkennung
City/Town: Erlangen, Bayern, Germany
Book Volume: 50
Edition: Studien zur Mustererkennung
ISBN: 978-3-8325-5561-0
URI: https://logos-verlag.eu/cgi-bin/engbuchmid?isbn=5561&lng=eng&id=
This book addresses the automatic analysis of speech disorders resulting from a clinical condition (Parkinson's disease and hearing loss) or the natural aging process. For Parkinson's disease, the progression of speech symptoms is evaluated by considering speech recordings captured in the short-term (4 months) and long-term (5 years). Machine learning methods are used to perform three tasks: (1) automatic classification of patients vs. healthy speakers. (2) regression analysis to predict the dysarthria level and neurological state. (3) speaker embeddings to analyze the progression of the speech symptoms over time.
For hearing loss, automatic acoustic analysis is performed to evaluate whether the duration and onset of deafness (before or after speech acquisition) influence the speech production of cochlear implant users. Additionally, articulation, prosody, and phonemic analyses show that cochlear implant users present altered speech production even after hearing rehabilitation.
APA:
Arias Vergara, T. (2022). Analysis of Pathological Speech Signals. Erlangen, Bayern, Germany: Logos Verlag Berlin GmbH.
MLA:
Arias Vergara, Tomás. Analysis of Pathological Speech Signals. Erlangen, Bayern, Germany: Logos Verlag Berlin GmbH, 2022.
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