Author

Chenda Li

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#artificial intelligence Jun 2026

TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation

TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost, is proposed and demonstrated that TF-MoE consistently improves separation performance under computation cost constraints.

Qin-Zhe Hu, Chenda Li, Wangyou Zhang et al. · arXiv.org · 3 citations · ⚡1

Preprint Aug 2026

Luna-TTS Family Technical Report

Luna-TTS Family, diffusion-language-model-based TTS systems pretrained on 1 million hours of speech across Chinese, English, Japanese, and Korean are proposed, which achieves the best results on most objective, model-based, and human-rated metrics for NVV and emotion control.

Feng Yin, Shuai Shi, Junjie Zheng et al. · 1 citation