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

Current speech language models generate responses directly without explicit reasoning, leading to errors that cannot be corrected once audio is produced. We introduce ``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.