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
Published: 2025-10-01 04:32:37

Abstract: 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.