Evaluation of Neurogenic Voice Disorders

Dastolfo-Hromack, Christina; Walsh, Erin

doi:10.1007/978-3-030-28852-5_5

Cited by 2 publications

(1 citation statement)

References 63 publications

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“…A brief description of LeTalker's dynamics is The feedback controllers, due to the nature of their inputs, suffer from delays in the feedback loop. Physiologically, this delay is compensated by auditory planning [28]. The feedforward controller represents the auditory planning in the higher-order brain regions and generates motor commands from auditory targets.…”

Section: Letalkermentioning

confidence: 99%

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production

Palaparthi,

Alluri,

Titze

2024

Applied Sciences

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A computational neuromuscular control system that generates lung pressure and three intrinsic laryngeal muscle activations (cricothyroid, thyroarytenoid, and lateral cricoarytenoid) to control the vocal source was developed. In the current study, LeTalker, a biophysical computational model of the vocal system was used as the physical plant. In the LeTalker, a three-mass vocal fold model was used to simulate self-sustained vocal fold oscillation. A constant /ə/ vowel was used for the vocal tract shape. The trachea was modeled after MRI measurements. The neuromuscular control system generates control parameters to achieve four acoustic targets (fundamental frequency, sound pressure level, normalized spectral centroid, and signal-to-noise ratio) and four somatosensory targets (vocal fold length, and longitudinal fiber stress in the three vocal fold layers). The deep-learning-based control system comprises one acoustic feedforward controller and two feedback (acoustic and somatosensory) controllers. Fifty thousand steady speech signals were generated using the LeTalker for training the control system. The results demonstrated that the control system was able to generate the lung pressure and the three muscle activations such that the four acoustic and four somatosensory targets were reached with high accuracy. After training, the motor command corrections from the feedback controllers were minimal compared to the feedforward controller except for thyroarytenoid muscle activation.

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Section: Letalkermentioning

confidence: 99%

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production

Palaparthi,

Alluri,

Titze

2024

Applied Sciences

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What Can Altered Auditory Feedback Paradigms Tell Us About Vocal Motor Control in Individuals With Voice Disorders?

2022

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Purpose: The goal of this review article is to provide a summary of the progression of altered auditory feedback (AAF) as a method to understand the pathophysiology of voice disorders. This review article focuses on populations with voice disorders that have thus far been studied using AAF, including individuals with Parkinson's disease, cerebellar degeneration, hyperfunctional voice disorders, vocal fold paralysis, and laryngeal dystonia. Studies using AAF have found that individuals with Parkinson's disease, cerebellar degeneration, and laryngeal dystonia have hyperactive auditory feedback responses due to differing underlying causes. In persons with PD, the hyperactivity may be a compensatory mechanism for atypically weak feedforward motor control. In individuals with cerebellar degeneration and laryngeal dystonia, the reasons for hyperactivity remain unknown. Individuals with hyperfunctional voice disorders may have auditory–motor integration deficits, suggesting atypical updating of feedforward motor control. Conclusions: These findings have the potential to provide critical insights to clinicians in selecting the most effective therapy techniques for individuals with voice disorders. Future collaboration between clinicians and researchers with the shared objective of improving AAF as an ecologically feasible and valid tool for clinical assessment may provide more personalized therapy targets for individuals with voice disorders.

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Evaluation of Neurogenic Voice Disorders

Cited by 2 publications

References 63 publications

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production

What Can Altered Auditory Feedback Paradigms Tell Us About Vocal Motor Control in Individuals With Voice Disorders?

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