DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion

• URL: http://arxiv.org/abs/2601.22889v1
• Date: Fri, 30 Jan 2026 12:08:33 GMT
• Title: DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion
• Authors: Yuxuan Lou, Ziming Wu, Yaochen Wang, Yong Liu, Yingxuan Ren, Fuming Lai, Shaobing Lian, Jie Tang, Yang You,
• Abstract summary: We introduce textbfSilent Thought, Spoken Answer'' -- a paradigm where speech LLMs generate internal text reasoning alongside spoken responses.<n>We present method, the first diffusion-based speech-text language model supporting both understanding and generation.<n>Experiments show method achieves state-of-the-art speech-to-speech QA accuracy, outperforming the best baseline by up to 9 points.
• Score: 23.01044837428522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current speech language models generate responses directly without explicit reasoning, leading to errors that cannot be corrected once audio is produced. We introduce \textbf{``Silent Thought, Spoken Answer''} -- a paradigm where speech LLMs generate internal text reasoning alongside spoken responses, with thinking traces informing speech quality. To realize this, we present \method{}, the first diffusion-based speech-text language model supporting both understanding and generation, unifying discrete text and tokenized speech under a single masked diffusion framework. Unlike autoregressive approaches, \method{} jointly generates reasoning traces and speech tokens through iterative denoising, with modality-specific masking schedules. We also construct \dataset{}, the first speech QA dataset with paired text reasoning traces, containing 26K samples totaling 319 hours. Experiments show \method{} achieves state-of-the-art speech-to-speech QA accuracy, outperforming the best baseline by up to 9 points, while attaining the best TTS quality among generative models (6.2\% WER) and preserving language understanding (66.2\% MMLU). Ablations confirm that both the diffusion architecture and thinking traces contribute to these gains.

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