Dysarthria

About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.

Cerebral Blood Flow in Pure Dysarthria: Role of Frontal Cortical Hypoperfusion

Abstract: Background and Purpose—Isolated dysarthria, termed pure dysarthria, develops rarely after stroke, and its pathophysiology remains unclear. To clarify the underlying mechanism of pure dysarthria, we...

Home trials of a speech synthesizer in severe dysarthria: patterns of use, satisfaction and utility of word prediction.

Abstract: OBJECTIVE: The aim of this study was to evaluate a speech synthesizer with respect to patterns of use and satisfaction, during a 2-month trial at home, and the usefulness of the word prediction function. DESIGN: Prospective study. PARTICIPANTS: Of the 24 patients with severe dysarthria recruited, 10 completed the study. Five patients had cerebral palsy, 3 amyotrophic lateral sclerosis, one locked-in syndrome, and one anoxic brain damage. Mean age was 32 (standard deviation 21) years (range 9-66 years). METHODS: Each participant received 10 hours of training with the device (Dialo) and then used it at home for 2 months. The main outcome measures were: level of use recorded by the device, Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) satisfaction score (maximum = 5), and time needed to take dictations of standard-dictionary and personal-dictionary words with and without word prediction. RESULTS: Level of use varied widely across participants. Overall satisfaction at the end of the home trial was high, with a mean QUEST score of 3.4 (SD 1) and was related to the level of use of the device. Level of satisfaction at the end of the training session could not predict the level of use at home. No significant differences were found in dictation-taking times with and without word prediction. However, 6 of the 10 patients took dictation faster with than without word prediction. CONCLUSION: This study provides the first evidence supporting the benefits of a speech synthesizer used at home for several weeks. Word prediction is useful for some patients even if increase in dictation speed did not reach significance.

Subspace-Based Learning for Automatic Dysarthric Speech Detection

Abstract: To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of speech. Spectro-temporal subspaces are extracted using singular value decomposition, and dysarthric speech detection is achieved by applying a subspace-based discriminant analysis. Experimental results on databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed subspace-based approach using temporal subspaces is more advantageous than using spectral subspaces, also outperforming several state-of-the-art automatic dysarthric speech detection techniques.

Speech assistive technology to improve the interaction of dysarthric speakers with machines

Abstract: In this paper, we propose a communication scheme using automatic speech recognition and speech synthesis in order to assist dysarthric speakers. This system aims at improving recognition rate and intelligibility of dysarthric speech. An HMM-based recognizer using variable duration of Hamming window permits to raise the recognition rate of dysarthric speech up to 80 %. In order to improve the intelligibility of synthetic speech while keeping the naturalness close to the voice of dysarthric speaker, we synthesize the recognized text using new basic unit segmenter, a new concatenating algorithm and a grafting technique to correct the bad pronounced phonemes. The NEMOURS dysarthric database is used to evaluate the proposed assistive communication system. Results show that a rate of 65 % to 80% of correct word recognition and a Mean Opinion Score (MOS) of 4 were obtained.

Genomics of speech and language disorders

Abstract: There are multiple factors involved in speech and language. Investigating animal models, mainly through songbirds, have allowed a better understanding of the language process. Verbal dyspraxia, dysarthria, speech sound disorder, and stuttering are some examples of speech disorders, and specific language disorder, aphasia and, dyslexia of language disorders. More complex syndromes such as Autism-spectrum disorders, Down’s or Fragile X have more variable features. Genetic factors, such as hereditary or de novo mutations may be responsible for their development. In addition, most of them are involved in neurodevelopment with a huge range of molecular mechanisms and pathways that interact with each other, and there may be co-morbidity with other communication disorders or develop phenotypes unrelated to communication. Genes with heterogeneous functions in speech and language such as FOXP1, FOXP2, KIAA0319, ROBO1, APOE or CNTNAP2 are some examples. Epigenetic factors, especially miRNAs, influence their expressiveness. The genomics of these disorders allows us to understand language acquisition, carry out early detection strategies, genetic counseling and optimize future treatments, not only in communication disorders but also those neurological alterations that incorporate these mutations.