The Relationship Between Pitch Discrimination and Fundamental Frequency Variation: Effects of Singing Status and Vocal Hyperfunction

Aaron, Allison S.; Abur, Defne; Volk, Kalei P.; Noordzij, J. Pieter; Tracy, Lauren F.; Stepp, Cara E.

doi:10.1016/j.jvoice.2023.01.008

Cited by 2 publications

(1 citation statement)

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“…Effective oral communication is a basic and valued human daily activity [1,2]. A key aspect of this function is the sensory-motor integration for the control of speech production, which has been shown to be critical for speech acquisition [3] and that is affected in speech and voice disorders including vocal hyperfunction [4,5], stuttering and other disfluencies [6,7], as well as in neurodegenerative diseases (Parkinson's disease) [8,9].…”

Section: Introductionmentioning

confidence: 99%

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex

Cuadros,

Z-Rivera,

Castro

et al. 2023

Applied Sciences

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The neurocomputational model ‘Directions into Velocities of Articulators’ (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

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