Comparison of Acoustic and Textual Features for Dysarthria Severity Classification in Amyotrophic Lateral Sclerosis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0201584" target="_blank" >RIV/00216305:26230/26:0201584 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-archive.org/interspeech_2025/ys25_interspeech.pdf" target="_blank" >https://www.isca-archive.org/interspeech_2025/ys25_interspeech.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2025-2767" target="_blank" >10.21437/Interspeech.2025-2767</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Acoustic and Textual Features for Dysarthria Severity Classification in Amyotrophic Lateral Sclerosis

Popis výsledku v původním jazyce

We explore language-agnostic deep text embeddings for severity classification of dysarthria in Amyotrophic Lateral Sclerosis (ALS). Speech recordings are transcribed by human and ASR and embeddings of the transcripts are considered. Though speech recognition accuracy has been studied for grading dysarthria severity, no effort has yet been made to utilize text embeddings of the transcripts. We perform severity classification at different granularity (2, 3, and 5-class) using data obtained from 47 ALS subjects. Experiments with dense neural network based classifiers suggest that, though text features achieve nearly equal performances as baseline speech features, like statistics of mel frequency cepstral coefficients (MFCC), for 2-class classification, speech features outperform for higher number of classes. Concatenation of text embeddings and MFCC statistics attains the best performances with mean F1 scores of 88%, 68%, and 53%, respectively, in 2, 3, and 5-class classification.

Název v anglickém jazyce

Comparison of Acoustic and Textual Features for Dysarthria Severity Classification in Amyotrophic Lateral Sclerosis

Popis výsledku anglicky

We explore language-agnostic deep text embeddings for severity classification of dysarthria in Amyotrophic Lateral Sclerosis (ALS). Speech recordings are transcribed by human and ASR and embeddings of the transcripts are considered. Though speech recognition accuracy has been studied for grading dysarthria severity, no effort has yet been made to utilize text embeddings of the transcripts. We perform severity classification at different granularity (2, 3, and 5-class) using data obtained from 47 ALS subjects. Experiments with dense neural network based classifiers suggest that, though text features achieve nearly equal performances as baseline speech features, like statistics of mel frequency cepstral coefficients (MFCC), for 2-class classification, speech features outperform for higher number of classes. Concatenation of text embeddings and MFCC statistics attains the best performances with mean F1 scores of 88%, 68%, and 53%, respectively, in 2, 3, and 5-class classification.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

ISBN

—

ISSN

—

e-ISSN

2308-457X

Počet stran výsledku

5

Strana od-do

803-807

Název nakladatele

Isca-Int Speech Communication Assoc

Místo vydání

Rotterdam, The Netherlands

Místo konání akce

Rotterdam

Datum konání akce

17. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001585350500166