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Effects of speech cues in French-speaking children with dysarthria.

Erika S LevyGemma Moya-GaléYounghwa Michelle ChangLuca CampanelliAndrea A N MacleodSergio EscorialChristelle Maillart
Published in: International journal of language & communication disorders (2020)
Speech cues targeting greater articulatory excursion and vocal intensity yield significant acoustic changes in French-speaking children with dysarthria. However, the changes may only aid listeners' ease of understanding at word level. The significant findings and great inter-speaker variability are generally consistent with studies on English-speaking children with dysarthria, although changes appear more constrained in these French-speaking children. What this paper adds What is already known on the subject According to the only study comparing effects of speech-cueing strategies on English-speaking children with dysarthria, intelligibility increases when the children are provided with cues aimed to increase articulatory excursion and vocal intensity. Little is known about speech characteristics in French-speaking children with dysarthria and no published research has explored effects of cueing strategies in this population. What this paper adds to existing knowledge This paper is the first study to examine the effects of speech cues on the acoustics and intelligibility of French-speaking children with CP. It provides evidence that the children can make use of cues to modify their speech, although the changes may only aid listeners' ease of understanding at word level. What are the potential or actual clinical implications of this work? For clinicians, the findings suggest that speech cues emphasizing increasing articulatory excursion and vocal intensity show promise for improving the ease of understanding of words produced by francophone children with dysarthria, although improvements may be modest. The variability in the responses also suggests that this population may benefit from a combination of such cues to produce words that are easier to understand.
Keyphrases
  • young adults
  • machine learning
  • drug delivery
  • deep learning
  • human health