New distillation method boosts ASR model performance with less data

Researchers have developed a data-efficient method for training automatic speech recognition (ASR) models, specifically focusing on a 0.6B parameter model named Ark-ASR. By employing on-policy distillation from a larger Qwen-ASR teacher model, they were able to significantly improve Ark-ASR's performance on Mandarin and English benchmarks. This approach requires substantially less supervised audio data compared to existing methods, demonstrating that teacher-guided training can effectively enhance smaller ASR models. AI

RANK_REASON The cluster contains an academic paper detailing a new method for training ASR models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Ark-ASR
• Qwen3-ASR-0.6B
• Qwen3-ASR-1.7B
• Qwen3-Omni AuT
• Qwen-ASR

• paper
• model release

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