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arXiv · 2606.21635

Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement

Nasser-Eddine Monir·Paul Magron·Romain Serizel
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Abstract

Conventional training losses for speech enhancement based on the signal-to-distortion ratio (SDR) treat all time-frequency (TF) regions uniformly, overlooking the fine-grained spectral cues that are relevant to specific phoneme intelligibility. We propose a TF weighting framework that modulates the SDR objective based on local speech presence, speech-to-interference ratio (SIR), and spectral flux. By integrating these factors into a differentiable objective, the framework emphasizes TF bins with high speech-noise competition while also accounting for transient cues such as consonant bursts. Experimental results show that our approach improves objective frequency-weighted enhancement metrics, as well as phoneme recognition accuracy, particularly for consonants. Spectral analysis shows better reconstruction of mid-frequency structures at less adverse SIR.

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Nasser-Eddine Monir, Paul Magron, Romain Serizel. 2026-06-19. Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement. https://arxiv.org/abs/2606.21635

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