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Deep Learning for Neuromuscular Control of Vocal Source for Voice Production.

Anil Palaparthi1, Rishi K Alluri2, Ingo R Titze1

  • 1Utah Center for Vocology, University of Utah, Salt Lake City, UT 84112, USA.

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|July 29, 2024
PubMed
Summary

A deep-learning control system accurately manages lung pressure and laryngeal muscles for voice production. This computational model achieves precise acoustic and somatosensory targets, crucial for speech synthesis and vocal research.

Keywords:
TensorFlowartificial neural networksnonlinear control systemsspeech acousticsvoice production

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Area of Science:

  • Computational biophysics
  • Speech science
  • Neuroscience

Background:

  • Developing accurate computational models of the human vocal system is essential for understanding speech production.
  • Neuromuscular control systems play a critical role in regulating vocal parameters.
  • Previous models often lack integrated acoustic and somatosensory feedback mechanisms.

Purpose of the Study:

  • To develop and evaluate a deep-learning-based neuromuscular control system for voice production.
  • To integrate acoustic and somatosensory feedback for precise control of vocal parameters.
  • To utilize the LeTalker biophysical model for simulating and training the control system.

Main Methods:

  • A biophysical computational model (LeTalker) with a three-mass vocal fold model was employed.
  • A deep-learning control system with acoustic feedforward and feedback controllers was designed.
  • The system was trained on 50,000 steady speech signals generated by LeTalker.

Main Results:

  • The control system accurately achieved four acoustic targets (fundamental frequency, sound pressure level, spectral centroid, signal-to-noise ratio).
  • The system also accurately met four somatosensory targets (vocal fold length, fiber stress).
  • Feedback controller corrections were minimal post-training, except for thyroarytenoid muscle activation.

Conclusions:

  • The developed deep-learning control system effectively regulates lung pressure and laryngeal muscle activations.
  • The model demonstrates high accuracy in achieving both acoustic and somatosensory targets for voice control.
  • This approach offers a robust framework for computational modeling of speech production and vocal motor control.