Purpose: Generalization of perceptual learning has received limited attention in listener adaptation studies with dysarthric speech. This study investigated whether adaptation to a talker with dysarthria could be predicted by the nature of the listener's prior familiarization experience, specifically similarity of perceptual features, and level of intelligibility.
Method: Following an intelligibility pretest involving a talker with ataxic dysarthria, 160 listeners were familiarized with 1 of 7 talkers with dysarthria-who differed from the test talker in terms of perceptual similarity (same, similar, dissimilar) and level of intelligibility (low, mid, high)-or a talker with no neurological impairment (control). Listeners then completed an intelligibility posttest on the test talker.
Results: All listeners benefited from familiarization with a talker with dysarthria; however, adaptation to the test talker was superior when the familiarization talker had similar perceptual features and reduced when the familiarization talker had low intelligibility.
Conclusion: Evidence for both generalization and specificity of learning highlights the differential value of listeners' prior experiences for adaptation to, and improved understanding of, a talker with dysarthria. These findings broaden our theoretical knowledge of adaptation to degraded speech, as well as the clinical application of training paradigms that exploit perceptual processes for therapeutic gain.
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http://dx.doi.org/10.1044/2017_JSLHR-S-17-0127 | DOI Listing |
J Speech Lang Hear Res
October 2024
Department of Communication Sciences and Disorders, The University of Iowa, Iowa City.
Purpose: Error related to incorrect use of rating scales is problematic in the assessment and treatment of dysarthria. The main purpose of this project was to determine scale fit for cardinal speech features of hypokinetic dysarthria. A secondary aim was to determine rater reliability for the two different scales explored.
View Article and Find Full Text PDFFront Hum Neurosci
February 2024
Department of Clinical Neurological Sciences, University Hospital, London, ON, Canada.
Speech rate reduction is a global speech therapy approach for speech deficits in Parkinson's disease (PD) that has the potential to result in changes across multiple speech subsystems. While the overall goal of rate reduction is usually improvements in speech intelligibility, not all people with PD benefit from this approach. Speech rate is often targeted as a means of improving articulatory precision, though less is known about rate-induced changes in other speech subsystems that could help or hinder communication.
View Article and Find Full Text PDFBrain Sci
October 2022
Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
This study pursued two goals: (1) to establish range of motion (ROM) demand tiers (i.e., low, moderate, high) specific to the jaw (J), lower lip (LL), posterior tongue (PT), and anterior tongue (AT) for multisyllabic words based on the articulatory performance of neurotypical talkers and (2) to identify demand- and disease-specific articulatory performance characteristics in talkers with amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).
View Article and Find Full Text PDFBrain Sci
April 2022
Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Although researchers have recognized the need to better account for the heterogeneous perceptual speech characteristics among talkers with the same disease, guidance on how to best establish such dysarthria subgroups is currently lacking. Therefore, we compared subgroup decisions of two data-driven approaches based on a cohort of talkers with Huntington's disease (HD): (1) a statistical clustering approach (STATCLUSTER) based on perceptual speech characteristic profiles and (2) an auditory free classification approach (FREECLASS) based on listeners' similarity judgments. We determined the amount of overlap across the two subgrouping decisions and the perceptual speech characteristics driving the subgrouping decisions of each approach.
View Article and Find Full Text PDFFront Artif Intell
December 2021
Department of Communication Sciences and Disorders, Long Island University, Brooklyn, NY, United States.
The sophistication of artificial intelligence (AI) technologies has significantly advanced in the past decade. However, the observed unpredictability and variability of AI behavior in noisy signals is still underexplored and represents a challenge when trying to generalize AI behavior to real-life environments, especially for people with a speech disorder, who already experience reduced speech intelligibility. In the context of developing assistive technology for people with Parkinson's disease using automatic speech recognition (ASR), this pilot study reports on the performance of Google Cloud speech-to-text technology with dysarthric and healthy speech in the presence of multi-talker babble noise at different intensity levels.
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