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Updated: Jul 23, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
Published on: June 2, 2010
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.
Abstract:
Laser doppler vibrometers (LDVs) facilitate noncontact speech acquisition; however, they are prone to material-dependent spectral distortions and speckle noise, which degrade intelligibility in noisy environments. This study proposes a data augmentation method that incorporates material-specific and impulse noises to simulate LDV-induced distortions. The proposed approach utilizes a gated convolutional neural network with HiFi-GAN to enhance speech intelligibility across various material and low signal-to-noise ratio (SNR) conditions, achieving a short-time objective intelligibility score of 0.76 at 0 dB SNR. These findings provide valuable insights into optimized augmentation and deep-learning techniques for enhancing LDV-based speech recordings in practical applications.
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