This study examined fluency enhancement in people who stutter via the concomitant presentation of silently mouthed visual speech. Ten adults who stutter recited memorized text while watching another speaker silently mouth linguistically equivalent and linguistically different material. Relative to a control condition, in which no concomitant stimulus was provided, stuttering was reduced by 71% in the linguistically equivalent condition versus only 35% in the linguistically different condition. Despite being an 'incomplete' second speech signal, visual speech possesses the capacity to immediately and substantially enhance fluency when it is linguistically equivalent to the intended utterance. It is suggested that fluency enhancement via concomitantly presented external speech is achieved through the extraction of relevant speech gestures from the external speech signal that compliment the intended production, thereby compensating for possible internal inconsistencies in the matching of speech codes in people who stutter. As visual speech perception relies on fewer redundant cues to demarcate the intended gestures, when used as an external stuttering inhibitor, higher degrees of linguistic equivalence seem to be necessary for optimal stuttering inhibition.
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http://dx.doi.org/10.1076/jcen.26.2.161.28090 | DOI Listing |
Netw Neurosci
December 2024
Department of Clinical Cognition Science, Clinic of Neurology at the RWTH Aachen University Faculty of Medicine, ZBMT, Aachen, Germany.
Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals.
View Article and Find Full Text PDFMed J Armed Forces India
December 2024
Associate Professor, Dayanand Sagar Univerity, Bengaluru, India.
Background: Vital information about a person's physical and emotional health can be perceived in their voice. After sleep loss, altered voice quality is noticed. The circadian rhythm controls the sleep cycle, and when it is askew, it results in fatigue, which is manifested in speech.
View Article and Find Full Text PDFZh Nevrol Psikhiatr Im S S Korsakova
December 2024
Novosibirsk State Medical University, Novosibirsk, Russia.
Objective: To evaluate the effectiveness of complex rehabilitation measures using the drug Cortexin in children with neuropsychiatric pathology during a one-year follow-up.
Material And Methods: A promising dynamic examination and treatment of 323 children with neuropsychiatric pathology from the age of 7 days to 1 year, age 3.2±1.
Eur J Neurol
January 2025
School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Background: The regulatory role of the apolipoprotein E (APOE) ε4 allele in the clinical manifestations of spinocerebellar ataxia type 3 (SCA3) remains unclear. This study aimed to evaluate the impact of the APOE ε4 allele on cognitive and motor functions in SCA3 patients.
Methods: This study included 281 unrelated SCA3 patients and 182 controls.
Am J Otolaryngol
December 2024
Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin 300192, China; Institute of Otolaryngology of Tianjin, Tianjin, China; Key Laboratory of Auditory Speech and Balance Medicine, Tianjin, China; Key Clinical Discipline of Tianjin (Otolaryngology), Tianjin, China; Otolaryngology Clinical Quality Control Centre, Tianjin, China.
Purpose: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Materials And Methods: The experiment was based on 3057 images (normal, glottic cancer, granuloma, Reinke's Edema, vocal cord cyst, leukoplakia, nodules and polyps) from the dataset Laryngoscope8. A classification model based on deep neural networks was developed and tested.
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