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Controllable Conformer for Speech Enhancement and Recognition

Authors :
Guo, Zilu
Du, Jun
Siniscalchi, Sabato Marco
Pan, Jia
Liu, Qingfeng
Source :
IEEE Signal Processing Letters; 2025, Vol. 32 Issue: 1 p156-160, 5p
Publication Year :
2025

Abstract

We propose a novel approach to speech enhancement, termed Controllable ConforMer for Speech Enhancement (CCMSE), which leverages a Conformer-based architecture integrated with a control factor embedding module. Our method is designed to optimize speech quality for both human auditory perception and automatic speech recognition (ASR). It is observed that while mild denoising typically preserves speech naturalness, stronger denoising can improve human auditory tasks but often at the cost of ASR accuracy due to increased distortion. To address this, we introduce an algorithm that balances these trade-offs. By utilizing differential equations to interpolate between outputs at varying levels of denoising intensity, our method effectively combines the robustness of mild denoising with the clarity of stronger denoising, resulting in enhanced speech that is well-suited for both human and machine listeners. Experimental results on the CHiME-4 dataset validate the effectiveness of our approach.

Details

Language :
English
ISSN :
10709908 and 15582361
Volume :
32
Issue :
1
Database :
Supplemental Index
Journal :
IEEE Signal Processing Letters
Publication Type :
Periodical
Accession number :
ejs68383266
Full Text :
https://doi.org/10.1109/LSP.2024.3505794