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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

¹Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.

JASA Express Letters

|April 2, 2025

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.