A model for sibilant distortion detection in children

Ivo Anjos, Margarida Grilo, Mariana Ascensão, Isabel Guimarães, João Magalhães, Sofia Cavaco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


The distortion of sibilant sounds is a common type of speech sound disorder in European Portuguese speaking children. Speech and language pathologists (SLP) use different types of speech production tasks to assess these distortions. One of these tasks consists of the sustained production of isolated sibilants. Using these sound productions, SLPs usually rely on auditory perceptual evaluation to assess the sibilant distortions. Here we propose to use an isolated sibilant machine learning model to help SLPs assess these distortions. Our model uses Mel frequency cepstral coefficients of the isolated sibilant phones from 145 children, and was trained using support vector machines. The analysis of the false negatives detected by the model can give insight into whether the child has a sibilant production distortion. We were able to confirm that there exists a relation between the model classification results and the distortion assessment of professional SLPs. Approximately 66% of the distortion cases identified by the model are confirmed by an SLP as having some sort of distortion or are perceived as being the production of a different sound.

Original languageEnglish
Title of host publicationDMIP 2018 - Proceedings of 2018 International Conference on Digital Medicine and Image Processing
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450365789
Publication statusPublished - 12 Nov 2018
Event2018 International Conference on Digital Medicine and Image Processing, DMIP 2018 - Okinawa, Japan
Duration: 12 Nov 201814 Nov 2018


Conference2018 International Conference on Digital Medicine and Image Processing, DMIP 2018


  • Machine learning
  • Sibilant sounds
  • Sigmatism assessment
  • Speech sound disorders

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