arXiv:2605.28139v1 Announce Type: new Abstract: Building competitive automatic speech recognition (ASR) models usually requires large-scale au- dio supervision, which makes reproduction and specialization expensive. We study Ark-ASR, a 0.6B- parameter audio-conditioned language model trained with 100k hours of speech, and examine whether a strong Qwen-ASR teacher can transfer additional recognition capability through on-policy distillation. Across Mandarin and English ASR benchmarks, the proposed training recipe consistently improves over supervised fine-tuning alone and outperforms the same-s
The increasing scale of ASR models necessitates more efficient training methods as data acquisition becomes a bottleneck and computational costs rise.
This development could significantly reduce the resources required to build and deploy competitive ASR and other large language models, making advanced AI more accessible and accelerating its adoption.
The barrier to entry for developing high-performance, specialized ASR models is lowered, potentially leading to more diverse applications and developers.
- · AI developers with limited data/compute
- · Companies seeking specialized ASR
- · Cloud AI service providers
- · Emerging market AI companies
- · Companies solely reliant on massive proprietary datasets for ASR advantage
- · Incumbent ASR providers slow to adopt distillation
Reduced cost and time for ASR model development through data-efficient techniques.
Proliferation of highly specialized and localized ASR applications across various industries.
Increased competition in AI model development due to lowered resource barriers, fostering innovation and potentially shifting market dynamics.
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Read at arXiv cs.AI