Raon-Speech Technical Report
Raon-Speech is a 9B-parameter speech language model for English and Korean, achieving top performance on speech understanding and generation while preserving text capabilities. Its full-duplex extension Raon-SpeechChat enables natural real-time conversation. The models are open-sourced.
Article intelligence
Key points
- Raon-Speech is a 9B-parameter SpeechLM trained on 1.38M hours of curated data.
- It outperforms eight similar models on speech tasks while retaining strong text QA performance.
- Raon-SpeechChat enables full-duplex conversation with turn-taking and interruption detection.
- All model checkpoints, training/inference pipeline, and demo are open-sourced.
Why it matters
This matters because raon-Speech is a 9B-parameter SpeechLM trained on 1.38M hours of curated data.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23912] Raon-Speech Technical Report
[Submitted on 8 Apr 2026]
Title:Raon-Speech Technical Report
View a PDF of the paper titled Raon-Speech Technical Report, by Beomsoo Kim and 25 other authors
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Abstract:We present Raon-Speech, a top-performing 9B-parameter speech language model (SpeechLM) for English and Korean speech understanding, answering, and generation, and Raon-SpeechChat, a high-performing full-duplex extension for natural real-time conversation. Raon-Speech successfully transforms a pre-trained LLM into a SpeechLM that both understands and generates speech while preserving strong text capabilities. It trains on 1.38M hours of highly curated English and Korean speech and text datasets with the following training stages: (1) speech modules alignment, (2) end-to-end SpeechLM pre-training with knowledge distillation, and (3) multi-task preference optimization-based post-training. Across 42 English and Korean speech and text benchmarks, Raon-Speech establishes the strongest overall profile on speech-centric tasks in our comparison against eight similarly sized recent audio foundation models, including Qwen2.5-Omni and Fun-Audio-Chat, while preserving strong text question answering performance. Building upon it, Raon-SpeechChat enables natural full-duplex conversation by continual training on 119K hours of time-aligned real and synthetic dialogue data. It proceeds through three complementary training stages: (1) causal encoder adaptation, (2) full-duplex pre-training, (3) full-duplex fine-tuning for voice and role-control. On multiple full-duplex benchmarks, Raon-SpeechChat shows its clearest strengths on the turn-taking and interruption-sensitive behaviors covered by FDB v1.0, and remains competitive across the broader full-duplex evaluation suite. We open-source all model checkpoints, the training and inference pipeline, and an interactive demo.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)
Cite as: arXiv:2605.23912 [cs.CL]
(or arXiv:2605.23912v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.23912
arXiv-issued DOI via DataCite
Submission history
From: Dongmin Park [view email] [v1] Wed, 8 Apr 2026 23:43:46 UTC (679 KB)
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