arXiv:2606. 09677v1 Announce Type: cross Abstract: While discriminative models for multi-channel speech separation excel in reference-based metrics, they often exhibit suboptimal human listening quality.
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arXiv:2609.07226v1 Announce Type: cross Abstract: This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation m...
arXiv:2607. 00363v1 Announce Type: cross Abstract: Flow Matching (FM) has emerged as a powerful paradigm for speech generation but remains constrained by high inference latency and timbre leakage.
MeanVoiceFlow2 is a new voice conversion framework that jointly optimizes a flow-based conversion module and a computationally efficient content encoder. It is trained via conversion distillation from MeanVoiceFlow and real data reconstruction, and further enhanced with diffusion-GAN training, sample mixing, and teacher-guided conditioning augmentation. Experiments on zero-shot voice conversion show that MeanVoiceFlow2 delivers higher perceptual quality and about nine times faster inference than its predecessor while preserving speaker similarity.
arXiv:2607. 02119v1 Announce Type: cross Abstract: While Large Multimodal Models excel in comprehension, high-throughput inference engines lack native support for multimodal generation.
arXiv:2606. 13121v1 Announce Type: cross Abstract: Simultaneous speech-to-speech translation aims to enable near-real-time communication by minimizing latency, offering a compelling, real-time alternative to the high latency of consecutive translation.
arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.