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Controllable Conformer for Speech Enhancement and Recognition
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AI
DOI：10.1109/LSP.2024.3505794.png)
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Abstract
En 中文
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.
Keywords:
Noise reduction
Speech enhancement
Signal to noise ratio
Estimation
Speech recognition
Noise measurement
Schedules
Differential equations
Decoding
Computer architecture
robust speech recognition
controllable denoising intensity
ASR
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