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SLINet: Dysphasia detection in children using deep neural network

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    SLINet: Dysphasia detection in children using deep neural network

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20202 - Communication engineering and systems

Result continuities

  • Project

    <a href="/en/project/VI04000039" target="_blank" >VI04000039: Early COVID-19 infection detection system for the safety of vulnerable groups using artificial intelligence</a><br>

  • Continuities

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

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    BIOMED SIGNAL PROCES

  • ISSN

    1746-8094

  • e-ISSN

    1746-8108

  • Volume of the periodical

    July 2021

  • Issue of the periodical within the volume

    68

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    13

  • Pages from-to

    1-13

  • UT code for WoS article

    000670369400002

  • EID of the result in the Scopus database

    2-s2.0-85107839032