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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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Journal ArticleDOI
TL;DR: Video is presented of 3 sibling cases of LOTS in which a dysarthric stutter was the sole presenting symptom in order to better characterize the phenotype of this disease.
Abstract: Late-onset Tay-Sachs disease (LOTS) is a rare autosomal-recessive genetic disorder caused by insufficient activity of the lysosomal enzyme, beta-hexosaminidase A, resulting in intracellular accumulation of gangliosides in the central nervous system. Clinical manifestations can include unsteadiness in gait, muscle weakness, cognitive dysfunction, psychiatric disturbance, and dysarthric speech. The variable presentation of these symptoms, combined with the late onset of the disease, often results in misdiagnosis. We present video of 3 sibling cases of LOTS in which a dysarthric stutter was the sole presenting symptom in order to better characterize the phenotype of this disease.

10 citations

Journal ArticleDOI
26 Jun 2018-Trials
TL;DR: The purpose of the present study was to compare the effects of LQG with traditional breathing training (combined with basic articulation training in both groups) in patients with post-stroke dysarthria to provide evidence about the effectiveness of L QG for improvement of speech breathing function and speech ability.
Abstract: Stroke-induced dysarthria is caused by muscle weakness, sacral or muscular dystonia, and incoordination of the articulatory organ formed by organic lesions caused by cerebral vascular obstruction or sudden bursting of blood vessels in the brain, which may cause abnormal breathing patterns, pronunciation, resonance, rhythm, and unclear articulation. The Six Character Formula, or Liuzijue qigong (LQG), is an essential part of Chinese traditional exercises and focuses on breathing–speech synchronization. The purpose of the present study was to compare the effects of LQG with traditional breathing training (combined with basic articulation training in both groups) in patients with post-stroke dysarthria. The proposed study will be a single-center randomized controlled trial. A total of 100 patients, with a modified Frenchay Dysarthria Assessment (FDA) dysarthria assessment score < 27 and with a FDA speech breathing level ≥ b will be randomly divided into study (LQG, n = 50) and control (conventional breathing training, n = 50) groups. Basic articulation training will be conducted once a day, five times a week for 3 weeks. Data collection will be conducted at baseline, 1 week, and 2 weeks post-treatment initiation and after completion of the treatment (3 weeks). Comprehensive analyses will be conducted to measure and compare any differences in speech breathing dysfunction levels, comprehensive evaluation of dysarthria, maximum phonation time (MPT), maximal counting ability, signal-noise (S/Z) ratio, and loudness scales between the study and control groups. This trial will provide evidence about the effectiveness of LQG for improvement of speech breathing function and speech ability in patients with post-stroke dysarthria complicated with abnormal breathing. Chinese Clinical Trial Registry, ChiCTR-INR-16010215. Registered 21 December 2016.

10 citations

Journal ArticleDOI
TL;DR: This work identifies the articulatory errors of each dysarthric speaker using isolated-style phoneme recognition system trained with TIMIT speech corpus, followed by product of likelihood Gaussian-based analysis and results are quite encouraging.
Abstract: Dysarthria is a motor speech disorder that causes inability to control and coordinate one or more articulators. This makes it difficult for a dysarthric speaker to utter certain speech sound units, thereby producing poorly articulated, slurred, and unintelligible speech. Hence, a speech supportive system needs to be developed to support them in their social difficulties. The current work aims at developing a speech supportive system, the objectives of which are threefold, namely (i) identifying the articulatory errors of each dysarthric speaker, (ii) developing a speech recognition system that corrects the errors in dysarthric speech by incorporating the findings from the first fold using a speaker-specific dictionary and (iii) developing an HMM-based speaker-adaptive speech synthesis system that synthesizes the error-corrected text for each dysarthric speaker retaining their identity. In the current work, the articulatory errors are analysed and identified, for 10 dysarthric speakers from the Nemours dysarthric speech corpus, using isolated-style phoneme recognition system trained with TIMIT speech corpus, followed by product of likelihood Gaussian-based analysis. The estimated articulatory errors are incorporated into a phoneme recognition system using speaker-specific dictionary and bigram language model. The error-corrected text is then synthesized as speech. The synthesized speech is evaluated to check its intelligibility and naturalness using mean opinion score. To further improve the intelligibility, speech rate of the synthesized speech is modified using time-domain pitch synchronous overlap add (TDPSOLA) technique. The results are quite encouraging, and this system is expected to be developed as a speech assistive device for a large vocabulary, in the near future, in a hand-held device.

10 citations

Journal Article
TL;DR: A 55-year-old right-handed Japanese man with motor neuron disease and dysgraphia of kana letters did not show severe dementia in his early stage of his disease, but developed it later in the disease's progression, and agraphia might be due to the atrophic changes in the temporal lobe.
Abstract: We report a 55-year-old right-handed Japanese man with motor neuron disease and dysgraphia of kana letters. He was admitted to our hospital because of dysarthria and dysphasia. On admission, the results of general physical examination were within normal limits. Neurological examination revealed severe dysarthria, dysphasia, impaired movement of the tongue without fasciculation and slight distal muscle weakness in the bilateral upper limbs. There were no fasciculation of the muscle. Deep tendon reflexes were hyperactive without Babinski's signs. Sensation, coordination, and gait were normal. Neurophysiological studies demonstrated normal motor nerve conduction velocities and sensory action potential. The results of needle electromyography of the upper limbs were compatible with motor neuron disease (MND). Magnetic resonance imaging (MRI) showed atrophy of the bilateral temporal region of the brain. 99mTc-HMPAO SPECT (Single Photon Emission Computed Tomography) showed reduced uptake of tracer in the bilateral temporal region. On neuropsychological examination, his behavior was normal, and orientation and intelligence were also preserved, but his speech was severely impaired. Reading comprehension was slightly impaired. In regard to writing comprehension, he had no difficulty in copying of words though dictation was found to be impaired. He omitted one kana letter in a word. Agraphia is accompanied by various factors such as aphasia, dementia, agnosia, alexia. But in this case at least for early stage, agraphia existed without other higher cortical dysfunction. He did not show severe dementia in his early stage of his disease, but developed it later in the disease's progression. In this case, agraphia might be due to the atrophic changes in the temporal lobe.

10 citations

Proceedings ArticleDOI
14 Sep 2014
TL;DR: This paper investigates how applying probabilistic dialogue management techniques can improve interaction performance of an environmental control system for users with moderate to severe dysathria and shows that a DM trained on data from multiple speakers outperform aDM trained onData from a single speaker.
Abstract: Spoken control interfaces are very attractive to people with severe physical disabilities who often also have a type of speech disorder known as dysarthria. This condition is known to decrease the accuracy of automatic speech recognisers (ASRs) especially for users with moderate to severe dysathria. In this paper we investigate how applying probabilistic dialogue management (DM) techniques can improve interaction performance of an environmental control system for such users. The effect of having access to different amounts of adaptation data, as well as using different vocabulary size for speakers of different intelligibilities is investigated. We explore the effect of adapting the DM models as the ASR performance increases, such as is the case in systems where more adaptation data is collected through system use. Improvements compared to a non-probabilistic DM baseline are seen both in terms of dialogue length and success rate, 9% and 25% mean relative improvement respectively. Looking at just the more severe dysarthric speakers these numbers rise 25% and 75% mean relative improvement. These improvements are higher when the ASR data adaptation amount is small. Further results show that a DM trained on data from multiple speakers outperform a DM trained on data from a single speaker.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023229
2022415
2021164
2020138
2019125
201888