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Extraction of Speech Features and Alignment to Detect Early Dyslexia Evidences

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Enterprise Information Systems (ICEIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 417))

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Abstract

Specific reading disorders are conditions caused by neurological dysfunctions that affect the linguistic processing of printed text. Many people go untreated due to the lack of specific tools and the high cost of using proprietary software; however, new audio signal processing technologies can help identify genetic pathologies. The methodology developed by medical specialists extracts characteristics from the reading of a text aloud and returns evidence of dyslexia. This work proposes an improvement of the research presented in [25], extracting new features and improvements serving as a tool for dyslexia indication efficiently. The analysis is done in recordings of the reading of pre-defined texts with school-age children. Direct and indirect characteristics of the audio signal are extracted. The direct ones are obtained through the methodology of separation of pauses and syllables. Simultaneously, the indirect characteristics are extracted through the alignment of audio signals, the Hidden Markov Model, and some heuristics of improvement. The indication of the probability of dyslexia is performed using a machine learning algorithm. The tests were compared with the specialist’s classification, obtaining high accuracy on the evidence of dyslexia. The difference between the values of the characteristics collected automatically and manually was below 20% for most features. Finally, the results show a promising research area for audio signal processing concerning the aid to specialists in the decision making related to language pathologies.

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Acknowledgements

This study was financed by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) and Universidade Federal de Ouro Preto (UFOP).

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Authors and Affiliations

  1. Computing Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    Fernanda M. Ribeiro, Alvaro R. Pereira Jr. & Andrea G. Campos Bianchi

  2. School of Computing, Federal University of Mato do Grosso do Sul, Campo Grande, Brazil

    Débora M. Barroso Paiva

  3. Speech and Language Therapy Department, Federal University of Minas Gerais, Belo Horizonte, Brazil

    Luciana M. Alves

Authors
  1. Fernanda M. Ribeiro
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  2. Alvaro R. Pereira Jr.
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  3. Débora M. Barroso Paiva
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  4. Luciana M. Alves
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  5. Andrea G. Campos Bianchi
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Corresponding author

Correspondence to Andrea G. Campos Bianchi .

Editor information

Editors and Affiliations

  1. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. George Mason University, Fairfax, VA, USA

    Alexander Brodsky

  4. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

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Ribeiro, F.M., Pereira, A.R., Paiva, D.M.B., Alves, L.M., Bianchi, A.G.C. (2021). Extraction of Speech Features and Alignment to Detect Early Dyslexia Evidences. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2020. Lecture Notes in Business Information Processing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-75418-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-75418-1_15

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