Assessing Speech Intelligibility and Severity Level in Parkinson's Disease Using Wav2Vec 2.0

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F24%3A00376500" target="_blank" >RIV/68407700:21460/24:00376500 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TSP63128.2024.10605915" target="_blank" >https://doi.org/10.1109/TSP63128.2024.10605915</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP63128.2024.10605915" target="_blank" >10.1109/TSP63128.2024.10605915</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Assessing Speech Intelligibility and Severity Level in Parkinson's Disease Using Wav2Vec 2.0

Popis výsledku v původním jazyce

Parkinson's disease (PD) is characterized by profound speech and intelligibility impairments. This paper investigates the potential of Wav2Vec 2.0, a pre-trained speech transformer-based model, in assessing speech intelligibility and severity levels in PD. By leveraging Wav2Vec 2.0 cross-language capabilities, we deployed an English model on Italian speech data and evaluated Character Error Rate (CER). Our dataset comprised Young Healthy Controls (YHC), Elderly Healthy Controls (EHC), and PD subjects. A significant difference in the mean CER (non-parametric ANOVA; p < 0.001) was observed, with YHC being significantly different from EHC and PD. Our analysis revealed that intelligibility in the PD group did not correlate significantly with Unified Parkinson's Disease Rating Scale (UPDRS) scores (Spearman's rho = 0.37, p = 0.07). Through Z-score based detection, we were able to identify the most affected PD subjects based on their intelligibility and ranked the words that were incorrectly recognized for these individuals.

Název v anglickém jazyce

Assessing Speech Intelligibility and Severity Level in Parkinson's Disease Using Wav2Vec 2.0

Popis výsledku anglicky

Parkinson's disease (PD) is characterized by profound speech and intelligibility impairments. This paper investigates the potential of Wav2Vec 2.0, a pre-trained speech transformer-based model, in assessing speech intelligibility and severity levels in PD. By leveraging Wav2Vec 2.0 cross-language capabilities, we deployed an English model on Italian speech data and evaluated Character Error Rate (CER). Our dataset comprised Young Healthy Controls (YHC), Elderly Healthy Controls (EHC), and PD subjects. A significant difference in the mean CER (non-parametric ANOVA; p < 0.001) was observed, with YHC being significantly different from EHC and PD. Our analysis revealed that intelligibility in the PD group did not correlate significantly with Unified Parkinson's Disease Rating Scale (UPDRS) scores (Spearman's rho = 0.37, p = 0.07). Through Z-score based detection, we were able to identify the most affected PD subjects based on their intelligibility and ranked the words that were incorrectly recognized for these individuals.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/LX22NPO5107" target="_blank" >LX22NPO5107: Národní ústav pro neurologický výzkum</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 47th International Conference on Telecommunications and Signal Processing (TSP)

ISBN

979-8-3503-6559-7

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

231-234

Název nakladatele

IEEE

Místo vydání

Montreal

Místo konání akce

Virtual Conference

Datum konání akce

10. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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