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Enhancing speech intelligibility in optical microphone systems through physics-informed data augmentation.

Jia-Wei Chen1, Jia-Hui Li1, Yi-Hao Jiang1

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Summary
This summary is machine-generated.

This study introduces a data augmentation method to improve speech clarity from Laser Doppler Vibrometers (LDVs). The technique enhances intelligibility in noisy environments by simulating material distortions and noise for better audio processing.

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Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning for Audio Enhancement
  • Biomedical Engineering

Background:

  • Laser Doppler Vibrometers (LDVs) enable noncontact speech acquisition but suffer from material-dependent distortions and speckle noise.
  • These distortions significantly degrade speech intelligibility, especially in noisy environments.

Purpose of the Study:

  • To propose a novel data augmentation method to simulate and mitigate LDV-induced speech distortions.
  • To enhance speech intelligibility for LDV recordings under various material and low signal-to-noise ratio (SNR) conditions.

Main Methods:

  • Developed a data augmentation technique incorporating material-specific and impulse noises to mimic LDV distortions.
  • Employed a gated convolutional neural network integrated with HiFi-GAN for speech enhancement.

Main Results:

  • Achieved a Short-Time Objective Intelligibility (STOI) score of 0.76 at 0 dB SNR.
  • Demonstrated significant improvement in speech intelligibility across diverse materials and low SNR scenarios.

Conclusions:

  • The proposed data augmentation and deep learning approach effectively enhances speech intelligibility from LDV recordings.
  • Offers valuable insights for optimizing augmentation strategies and deep learning models in practical LDV-based audio applications.