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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01 Oct 1996
TL;DR: A patient who exhibited mutism, then severe apraxia of speech with a concomitant hypokinetic, spastic, and ataxic dysarthria after administration of FK-506 is described, suggesting dysfunction of one or more neurochemical systems.
Abstract: The immunosuppressive agent FK-506 (tacrolimus) is one of the agents most commonly used to prevent rejection after liver transplantation. Neurologic toxicity related to FK-506 has been reported, including speech disorders; however, a detailed analysis of the speech disorder associated with use of FK-506 has not been presented. Herein we describe a patient who exhibited mutism, then severe apraxia of speech with a concomitant hypokinetic, spastic, and ataxic dysarthria after administration of FK-506. His residual mixed dysarthria, without radiographic evidence of a structural lesion, suggests dysfunction of one or more neurochemical systems. The pathophysiologic mechanisms underlying this intriguing entity remain obscure.
23 citations
TL;DR: Increased loudness and reduced rate exhibited differential effects on listeners' perceptual processing of dysarthric speech, and an error analysis suggested that listeners in the loud condition prioritized acoustic-phonetic cues in their attempts to resolve the degraded signal, whereas those in the slow condition appeared to preferentially weight lexical stress cues.
Abstract: Purpose To determine how increased vocal loudness and reduced speech rate affect listeners' cognitive–perceptual processing of hypokinetic dysarthric speech associated with Parkinson's disease. Met...
23 citations
12 May 2019
TL;DR: The authors proposed a neural network that can learn a filterbank, a normalization factor and a compression power from the raw speech, jointly with the rest of the architecture for dysarthria detection from sentence-level audio recordings.
Abstract: Speech classifiers of paralinguistic traits traditionally learn from diverse hand-crafted low-level features, by selecting the relevant information for the task at hand. We explore an alternative to this selection, by learning jointly the classifier, and the feature extraction. Recent work on speech recognition has shown improved performance over speech features by learning from the waveform. We extend this approach to paralinguistic classification and propose a neural network that can learn a filterbank, a normalization factor and a compression power from the raw speech, jointly with the rest of the architecture. We apply this model to dysarthria detection from sentence-level audio recordings. Starting from a strong attention-based baseline on which mel-filterbanks outperform standard low-level descriptors, we show that learning the filters or the normalization and compression improves over fixed features by 10% absolute accuracy. We also observe a gain over OpenSmile features by learning jointly the feature extraction, the normalization, and the compression factor with the architecture. This constitutes a first attempt at learning jointly all these operations from raw audio for a speech classification task.
23 citations
TL;DR: Results showed that although all of the cerebellar-diseased subjects had normal total lung capacities, almost half had vital capacities below normal limits, and irregularities in the chest wall movements exhibited by the control subjects suggesting that their presence was caused by the Cerebellar disease.
Abstract: The respiratory abilities of a group of 12 speech disordered subjects with cerebellar disease were assessed using both spirometric and kinematic techniques and compared to those of a group of 12 no...
23 citations
TL;DR: These data suggest that people with DS and dysarthria can respond positively to intensive speech treatment such as LSVT, and further investigations are needed to develop speech treatments specific to DS.
Abstract: Objective: This study investigated the impact of an established behavioural dysarthria treatment on acoustic and perceptual measures of speech in two adults with Down syndrome (DS) and dysarthria to obtain preliminary measures of treatment effect, effect size and treatment feasibility.Methods: A single-subject A-B-A experimental design was used to measure the effects of the Lee Silverman Voice treatment (LSVT®) on speech in two adults with DS and dysarthria. Dependent measures included vocal sound pressure level (dB SPL), phonatory stability and listener intelligibility scores.Results: Statistically significant improvements (p < 0.05) in vocal dB SPL and phonatory stability were present following treatment in both participants. Speech intelligibility scores improved in one of the two participants.Conclusions: These data suggest that people with DS and dysarthria can respond positively to intensive speech treatment such as LSVT. Further investigations are needed to develop speech treatments specific to DS.
23 citations