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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Pattern Recognition Letters

ISSN

0167-8655

e-ISSN

1872-7344

Volume of the periodical

150

Issue of the periodical within the volume

October

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

8

Pages from-to

272-279

UT code for WoS article

000694715500014

EID of the result in the Scopus database

2-s2.0-85105246597