Topic
Dysarthria
About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.
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TL;DR: Two cases are described in which speech therapists and psychologists collaborated in behaviour therapy for patients with communication disorders after brain-damage, suggesting that there are advantages in not making a sharp demarcation of roles between psychologists and speech therapists in the mental rehabilitation of the brain-damaged.
Abstract: SummaryTwo cases are described in which speech therapists and psychologists collaborated in behaviour therapy for patients with communication disorders after brain-damage. Anxiety reduction techniques were used with a woman with mild aphasia, and social skills training with a man with dysarthria. It is suggested that there are advantages in not making a sharp demarcation of roles between psychologists and speech therapists in the mental rehabilitation of the brain-damaged.
3 citations
TL;DR: The groups of healthcare professionals who work with dysarthria are more likely to understand the PD patients' speech than the groups of naïve listeners.
Abstract: Speech intelligibility, how well a listener comprehends the speaker’s message, is related to the listener’ expertise and type of the message conveyed. There is no evidence about speech intelligibil...
3 citations
19 Feb 2021
TL;DR: In this article, the authors used different time-frequency representations for voiced and unvoiced segments and used them for intelligibility assessment with CNN classifier and combined the scores obtained by the two systems to assign an intelligibility level.
Abstract: The intelligibility assessment of dysarthric speech is essential for planning therapy. Time-frequency representations have been used for automatic intelligibility assessment of dysarthria. These representations have been derived from the utterance as a whole. As voiced and unvoiced components have different characteristics; in this work, we use different time-frequency representations for voiced and unvoiced segments and use them for intelligibility assessment with CNN classifier. Finally, we combine the scores obtained by the two systems to assign an intelligibility level. The combined system’s performance is found to be superior to the systems that used both voiced and unvoiced components separately or together as one utterance. The utterances of the TORGO database are used in the intelligibility assessment. Automatic assessment of speech intelligibility reduces speech-language pathologists’ time and effort in assisting diagnosis and treatment design.
3 citations
Journal Article•
3 citations