Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351360" target="_blank" >RIV/68407700:21230/21:00351360 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.patrec.2021.04.011" target="_blank" >https://doi.org/10.1016/j.patrec.2021.04.011</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.patrec.2021.04.011" target="_blank" >10.1016/j.patrec.2021.04.011</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages

Popis výsledku v původním jazyce

Patients suffering from neurodegenerative disorders such as Parkinson's or Huntington's disease exhibit speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows to develop computer aided tools to support the diagnosis and to evaluate the disease severity, which helps clinicians to make timely decisions about the treatment of the patients. This paper extends our previous studies about methods to classify patients with neurodegenerative diseases from speech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy to classify different speech impairments in patients that are native of different languages. The transfer learning schemes aim to improve the accuracy of the models when the weights of a neural network are initialized with utterances from a different corpus than the one used for the test set. The proposed methodology is evaluated with speech data from Parkinson's disease patients, who are Spanish, German, and Czech native speakers, Huntington's disease patients, who are Czech native speakers, and English native speakers affected by laryngeal impairments. We performed experiments in two scenarios: (1) transfer learning among languages, where a base model is transferred to classify patients with the same disease, but who speak a different language, and (2) transfer learning among diseases, where the base model is transferred to a corpus from patients with a different disease. The results suggest that the transfer learning schemes improve the accuracy in the target corpus only when the base model is accurate enough to transfer the knowledge to the target corpus. This behavior is observed in different scenarios of both transfer learning among languages and diseases.

Název v anglickém jazyce

Transfer learning helps to improve the accuracy to classify patients with different speech disorders in different languages

Popis výsledku anglicky

Patients suffering from neurodegenerative disorders such as Parkinson's or Huntington's disease exhibit speech impairments that affect their communication capabilities. The automatic assessment of the speech of the patients allows to develop computer aided tools to support the diagnosis and to evaluate the disease severity, which helps clinicians to make timely decisions about the treatment of the patients. This paper extends our previous studies about methods to classify patients with neurodegenerative diseases from speech. The proposed approach considers convolutional neural networks trained with time frequency representations and a transfer learning strategy to classify different speech impairments in patients that are native of different languages. The transfer learning schemes aim to improve the accuracy of the models when the weights of a neural network are initialized with utterances from a different corpus than the one used for the test set. The proposed methodology is evaluated with speech data from Parkinson's disease patients, who are Spanish, German, and Czech native speakers, Huntington's disease patients, who are Czech native speakers, and English native speakers affected by laryngeal impairments. We performed experiments in two scenarios: (1) transfer learning among languages, where a base model is transferred to classify patients with the same disease, but who speak a different language, and (2) transfer learning among diseases, where the base model is transferred to a corpus from patients with a different disease. The results suggest that the transfer learning schemes improve the accuracy in the target corpus only when the base model is accurate enough to transfer the knowledge to the target corpus. This behavior is observed in different scenarios of both transfer learning among languages and diseases.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

1872-7344

Svazek periodika

150

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

272-279

Kód UT WoS článku

000694715500014

EID výsledku v databázi Scopus

2-s2.0-85105246597