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arXiv · 2606.18134

Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning

Alexander Polok·Samuele Cornell·Sathvik Udupa·Jan Černocký·Shinji Watanabe·Lukáš Burget
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Abstract

We propose diarization-conditioned spoken language models (SLMs), a strategy for extending SLMs to far-field multi-talker audio. Rather than adapting the decoder via Serialized Output Training, which risks catastrophic forgetting, we condition the acoustic encoder on diarization masks to extract target-speaker representations, keeping the decoder frozen. We instantiate this as Dixtral, integrating a Diarization Conditioned Whisper (DiCoW) encoder into the Voxtral SLM. On AMI, NOTSOFAR-1, LibriSpeechMix, and Mixer6, Dixtral outperforms Gemini 3.0 Flash, VibeVoice, and Voxtral Mini Transcribe V2 on speaker-attributed transcription by 29.0%, 19.8%, and 16.0% absolute cpWER respectively. On a novel long-form multi-speaker QA benchmark, zero-shot Dixtral matches Gemini on far-field content understanding, and when fine-tuned surpasses both Gemini and Voxtral operating on close-talk across all tasks.

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Alexander Polok, Samuele Cornell, Sathvik Udupa, Jan Černocký, Shinji Watanabe, Lukáš Burget. 2026-06-16. Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning. https://arxiv.org/abs/2606.18134

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