MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
Authors: name: Xingjian Zhao, name: Zhe Xu, name: Qinyuan Cheng, name: Zhaoye Fei, name: Luozhijie Jin, name: Yang Wang, name: Hanfu Chen, name: Yaozhou Jiang, name: Qinghui Gao, name: Ke Chen, name: Ruixiao Li, name: Mingshu Chen, name: Ruiming Wang, name: Wenbo Zhang, name: Yiyang Zhang, name: Donghua Yu, name: Yang Gao, name: Xiaogui Yang, name: Yitian Gong, name: Yuanfan Xu, name: Yaqian Zhou, name: Xuanjing Huang, name: Xipeng Qiu
Organization: OpenMOSS

Spoken dialogue systems often rely on cascaded pipelines that transcribe,
process, and resynthesize speech. While effective, this design discards
paralinguistic cues and limits expressivity. Recent end-to-end methods reduce
latency and better preserve these cues, yet still rely on text intermediates,
creating a fundamental bottleneck. We present MOSS-Speech, a true
speech-to-speech large language model that directly understands and generates
speech without relying on text guidance. Our approach combines a modality-based
layer-splitting architecture with a frozen pre-training strategy, preserving
the reasoning and knowledge of pretrained text LLMs while adding native speech
capabilities. Experiments show that our model achieves state-of-the-art results
in spoken question answering and delivers comparable speech-to-speech
performance relative to existing text-guided systems, while still maintaining
competitive text performance. By narrowing the gap between text-guided and
direct speech generation, our work establishes a new paradigm for expressive
and efficient end-to-end speech interaction.