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Objective: Dysarthric speech of persons with Huntington disease (HD) is typically described as hyperkinetic; however, studies suggest that dysarthria can vary and resemble patterns in other neurologic conditions. To test the hypothesis that distinct motor speech subgroups can be identified within a larger cohort of patients with HD, we performed a cluster analysis on speech perceptual characteristics of patient audio recordings.
Methods: Audio recordings of 48 patients with mild to moderate dysarthria due to HD were presented to 6 trained raters. Raters provided scores for various speech features (e.g., voice, articulation, prosody) of audio recordings using the classic Mayo Clinic dysarthria rating scale. Scores were submitted to an unsupervised k-means cluster analysis to determine the most salient speech features of subgroups based on motor speech patterns.
Results: Four unique subgroups emerged from the cohort of patients with HD. Subgroup 1 was characterized by an abnormally fast speaking rate among other unique speech features, whereas subgroups 2 and 3 were defined by an abnormally slow speaking rate. Salient speech features for subgroup 2 overlapped with subgroup 3; however, the severity of dysarthria differed. Subgroup 4 was characterized by mild deviations of speech features with typical speech rate. Length of CAG repeats, Unified Huntington's Disease Rating Scale total motor score, and percent intelligibility were significantly different for pairwise comparisons of subgroups.
Conclusion: This study supports the existence of distinct presentations of dysarthria in patients with HD, which may be due to divergent pathologic processes. The findings are discussed in relation to previous literature and clinical implications.
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http://dx.doi.org/10.1212/WNL.0000000000008541 | DOI Listing |
Biomed Phys Eng Express
September 2025
electrical engineering department, Indian Institute of Technology Roorkee, Research wing, electrical department, Roorkee, uttrakhand, 247664, INDIA.
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
September 2025
In this paper, we propose a novel framework, Combo, for harmonious co-speech holistic 3D human motion generation and efficient customizable adaption. In particular, we identify that one fundamental challenge as the multiple-input-multiple-output (MIMO) nature of the generative model of interest. More concretely, on the input end, the model typically consumes both speech signals and character guidance (e.
View Article and Find Full Text PDFHum Genet
September 2025
College of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital, Chinese PLA Medical School, 28 Fuxing Road, Beijing, 100853, China.
Recessive variants in TWNK cause syndromes arising from mitochondrial DNA (mtDNA) depletion. Hearing loss is the most prevalent manifestation in individuals with these disorders. However, the clinical and pathophysiological features have not been fully elucidated.
View Article and Find Full Text PDFMedicine (Baltimore)
September 2025
Department of Pediatrics, Affiliated Hospital of Jining Medical University, Jining, Shandong Province, China.
Rationale: Weaver syndrome is a rare congenital overgrowth disorder characterized by a wide spectrum of clinical manifestations that often overlap with other overgrowth syndromes. It is primarily caused by pathogenic variants in the Enhancer of Zeste Homolog 2 (EZH2) gene on chromosome 7q36.1.
View Article and Find Full Text PDFJ Acoust Soc Am
September 2025
Department of Linguistics, University of Iowa, Iowa City, Iowa 52242, USA.
This study focuses on suprasegmental features and investigates how the use of a second tonal dialect influences the production of tones in the first dialect among bidialectal speakers of Chengdu Mandarin (CM) and Standard Mandarin (SM). Using a word-naming task, this study analyzed the acoustic differences between tones in SM and CM that share similar pitch contours and assessed the impact of SM use on CM tone production. How bidialectal listeners perceptually map SM tones onto CM categories was further evaluated using a dissimilarity rating task.
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