LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement

Boyi Kang; Xinfa Zhu; Zihan Zhang; Zhen Ye; Mingshuai Liu; Ziqian Wang; Yike Zhu; Guobin Ma; Jun Chen; Longshuai Xiao; Chao Weng; Wei Xue; Lei Xie

arXiv · 2503.00493

LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement

Boyi Kang·Xinfa Zhu·Zihan Zhang·Zhen Ye·Mingshuai Liu·Ziqian Wang·Yike Zhu·Guobin Ma·Jun Chen·Longshuai Xiao·Chao Weng·Wei Xue·Lei Xie

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Abstract

Recent advancements in language models (LMs) have demonstrated strong capabilities in semantic understanding and contextual modeling, which have flourished in generative speech enhancement (SE). However, many LM-based SE approaches primarily focus on semantic information, often neglecting the critical role of acoustic information, which leads to acoustic inconsistency after enhancement and limited generalization across diverse SE tasks. In this paper, we introduce LLaSE-G1, a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. LLaSE-G1 offers the following key contributions: First, to mitigate acoustic inconsistency, LLaSE-G1 employs continuous representations from WavLM as input and predicts speech tokens from X-Codec2, maximizing acoustic preservation. Second, to promote generalization capability, LLaSE-G1 introduces dual-channel inputs and outputs, unifying multiple SE tasks without requiring task-specific IDs. Third, LLaSE-G1 outperforms prior task-specific discriminative and generative SE models, demonstrating scaling effects at test time and emerging capabilities for unseen SE tasks. Additionally, we release our code and models to support further research in this area.

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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie. 2025-06-10. LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement. https://arxiv.org/abs/2503.00493

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