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Updated: May 12, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
Published on: August 9, 2024
CROSS-DOMAIN DIFFUSION BASED SPEECH ENHANCEMENT FOR VERY NOISY SPEECH
Heming Wang1, DeLiang Wang1,2
1Department of Computer Science and Engineering, The Ohio State University, USA.
Abstract:
Deep learning based speech enhancement has achieved remarkable success, but challenges remain in low signal-to-noise ratio (SNR) nonstationary noise scenarios. In this study, we propose to incorporate diffusion-based learning into an enhancement model and improve robustness in extremely noisy conditions. Specifically, a frequency-domain diffusion-based generative module is employed, and it accepts the enhanced signal obtained from a time-domain supervised enhancement module as an auxiliary input to learn to recover clean speech spectrograms. Experimental results on the TIMIT dataset demonstrate the advantage of this approach and show better enhancement performance over other strong baselines in both -5 and -10 dB SNR noisy conditions.
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