Work

Cross-Domain Diffusion Based Speech Enhancement for Very Noisy Speech

Year: 2023

Type: conference-paper

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.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49357.2023.10096985

Authors: Heming Wang, DeLiang Wang

Institution: The Ohio State University

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Ohio Supercomputer Center, National Institute on Deafness and Other Communication Disorders

Awards: G6100668328, Speech segregation to improve intelligility of reverberant-noisy speech