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Time-Frequency Weighted Losses for Phoneme Reconstruction in DNN-Based Speech Enhancement
Nasser-Eddine Monir, Paul Magron, Romain Serizel
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 spec…
Frequency-Weighted Training Losses for Phoneme-Level DNN-based Speech Enhancement
Nasser-Eddine Monir, Paul Magron, Romain Serizel
Recent advances in deep learning have significantly improved multichannel speech enhancement algorithms, yet conventional training loss functions such as the scale-invariant signal…
Evaluating Multichannel Speech Enhancement Algorithms at the Phoneme Scale Across Genders
Nasser-Eddine Monir, Paul Magron, Romain Serizel
Multichannel speech enhancement algorithms are essential for improving the intelligibility of speech signals in noisy environments. These algorithms are usually evaluated at the ut…
A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms
Nasser-Eddine Monir, Paul Magron, Romain Serizel
In the intricate acoustic landscapes where speech intelligibility is challenged by noise and reverberation, multichannel speech enhancement emerges as a promising solution for indi…