AI & ML
DualSpecSE: A Dual-Path Speech Enhancement Network Integrating Mel and Complex Spectrograms
arXiv cs.SD0 views
arXiv:2609.13911v2 Announce Type: replace-cross Abstract: In this paper, we propose DualSpecSE, a speech enhancement framework that jointly models Mel-spectrogram and complex spectrogram in a dual-path architecture for improved ASR performance and higher-quality speech reconstruction. The Mel branch learns coarse-grained acoustic representations and produces enhanced Mel-spectrograms for direct ASR usage, while the complex branch refines fine-grained spectral details for high-fidelity waveform reconstruction. Built upon the cross-band and narrow-band blocks from CleanMel, DualSpecSE introduces an interaction module and a fusion module to enable effective information exchange between the two branches. The model simultaneously outputs enhanced Mel and complex spectrogram without requiring a pretrained vocoder. Experimental results demonstrate consistent improvements in speech fidelity, perceptual quality, and ASR performance. Codes and audio samples are available.
Source:https://arxiv.org/abs/2609.13911PreviousAudio-Visual Turn-taking Prediction in Cocktail Party ScenariosNextIAPRepair: In-Network Aggregation Enhanced Proactive Repair for Erasure-Coded Storage System
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