MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
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.
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Xingjian Zhao
68e348e273e20ab577842086
Zhe Xu
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2025-10-09T06:52:03.863Z
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Xu Zhe
user
Phospheneser
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Qinyuan Cheng
claimed_verified
2025-10-08T08:03:15.149Z
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QinyuanCheng
user
Cqy2019
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Zhaoye Fei
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Luozhijie Jin
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Yang Wang
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Hanfu Chen
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Yaozhou Jiang
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Qinghui Gao
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Ke Chen
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Ruixiao Li
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Mingshu Chen
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2025-10-08T08:03:23.405Z
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Mingshu Chen
user
cms42
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Ruiming Wang
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Wenbo Zhang
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Yiyang Zhang
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Donghua Yu
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Yang Gao
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Xiaogui Yang
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Yitian Gong
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Yuanfan Xu
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Yaqian Zhou
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Xuanjing Huang
68e348e273e20ab57784209b
Xipeng Qiu
MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance
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.
MOSS-Speech is a speech-to-speech large language model that directly processes and generates speech without text intermediates, achieving state-of-the-art performance in spoken question answering and competitive text performance.
speech-to-speech large language model modality-based layer-splitting architecture frozen pre-training strategy spoken question answering