SLINet: Dysphasia detection in children using deep neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140934" target="_blank" >RIV/00216305:26220/21:PU140934 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1746809421003955?dgcid=author" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809421003955?dgcid=author</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2021.102798" target="_blank" >10.1016/j.bspc.2021.102798</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SLINet: Dysphasia detection in children using deep neural network

Popis výsledku v původním jazyce

A child has specific language impairment (SLI) or developmental dysphasia (DD) when the speech is delayed or has disordered language development for no apparent reason. As it may be related to loss of hearing, speech abnormality should be diagnosed at an early stage. The existing methods are mainly based on the utterance of vowels and have a high misclassification rate. This article proposes an automatic deep learning model that can be an effective tool to diagnose SLI at the early stage. In the proposed work, raw audio data is processed using Short-time Fourier transform and converted to decibel (dB) scaled spectrograms which are classified using the proposed convolutional neural network (CNN). This approach consists of utterances that contained seven types of vocabulary (vowels, consonant and different syllable Isolated words). A rigorous analysis based on different age-group was performed and a 10-fold Cross-Validation (CV) was done to test the accuracy of the classifier. A comprehensive experimental test reveals that 99.09 % of the children are correctly diagnosed by the proposed framework, which is superior when compared to state-of-the-art methods. The proposed scheme is gender and speaker-independent. The proposed model can be used as a stand-alone diagnostic tool that can assist automatic diagnosis of children for SLI and will be helpful for remote areas where professionals are not available. The proposed model is robust, efficient with low time complexity which is suitable for real-time applications.

Název v anglickém jazyce

SLINet: Dysphasia detection in children using deep neural network

Popis výsledku anglicky

A child has specific language impairment (SLI) or developmental dysphasia (DD) when the speech is delayed or has disordered language development for no apparent reason. As it may be related to loss of hearing, speech abnormality should be diagnosed at an early stage. The existing methods are mainly based on the utterance of vowels and have a high misclassification rate. This article proposes an automatic deep learning model that can be an effective tool to diagnose SLI at the early stage. In the proposed work, raw audio data is processed using Short-time Fourier transform and converted to decibel (dB) scaled spectrograms which are classified using the proposed convolutional neural network (CNN). This approach consists of utterances that contained seven types of vocabulary (vowels, consonant and different syllable Isolated words). A rigorous analysis based on different age-group was performed and a 10-fold Cross-Validation (CV) was done to test the accuracy of the classifier. A comprehensive experimental test reveals that 99.09 % of the children are correctly diagnosed by the proposed framework, which is superior when compared to state-of-the-art methods. The proposed scheme is gender and speaker-independent. The proposed model can be used as a stand-alone diagnostic tool that can assist automatic diagnosis of children for SLI and will be helpful for remote areas where professionals are not available. The proposed model is robust, efficient with low time complexity which is suitable for real-time applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/VI04000039" target="_blank" >VI04000039: Systém včasného záchytu infekce COVID-19 pro bezpečnost ohrožených skupin obyvatelstva s využitím umělé inteligence</a><br>

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

BIOMED SIGNAL PROCES

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

July 2021

Číslo periodika v rámci svazku

68

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000670369400002

EID výsledku v databázi Scopus

2-s2.0-85107839032