{"slug": "agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error", "title": "Agentic Data Sampling Framework for Multilingual ASR via Linguistic-Aware Error-Driven Optimization", "summary": "A new arXiv paper (2610.36538v1) proposes a linguistic-aware agentic data sampling framework that places a data-sampling LLM agent inside the multilingual ASR training loop, driven by a rubric analyzing errors from phonological, morphological and syntactic perspectives. Tested on twelve Common Voice languages with Whisper-medium as the ASR backbone, the method outperformed full-data training while using less training data and lower computational resources, and substantially accelerated training convergence versus conventional sampling strategies.", "body_md": "arXiv:2610.36538v1 Announce Type: new \nAbstract: Multilingual automatic speech recognition (ASR) has achieved rapid progress with large-scale pretrained models and multilingual speech corpora. Previous studies have proved the pivotal role of data sampling in multilingual ASR performance. However, existing sampling strategies largely rely on time-consuming manual, heuristic-based designs. To address this, we propose a linguistic-aware agentic data sampling framework that introduces a data sampling LLM agent into the multilingual ASR training loop. The agent is driven by a linguistically grounded rubric that analyzes ASR errors from phonological, morphological, and syntactic perspectives, and uses the resulting feedback together with training statistics, budget constraints, and algorithm profiles to select domain-level, sample-level, and token-level sampling strategies. This design turns evaluation results into adaptive data-selection decisions rather than treating WER/CER as final endpoints only. We conduct experiments on twelve Common Voice languages using Whisper-medium as the ASR backbone. The results show that our method achieves performance better than full-data training while using less training data and lower computational resources, and substantially accelerates training convergence compared to conventional sampling strategies.", "url": "https://wpnews.pro/news/agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error", "canonical_source": "https://arxiv.org/abs/2610.36538", "published_at": "2026-10-28 04:00:00+00:00", "updated_at": "2026-10-28 04:13:04.102707+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "natural-language-processing", "ai-agents", "ai-research"], "entities": ["arXiv", "Common Voice", "Whisper-medium"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error", "markdown": "https://wpnews.pro/news/agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error.md", "text": "https://wpnews.pro/news/agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error.txt", "jsonld": "https://wpnews.pro/news/agentic-data-sampling-framework-for-multilingual-asr-via-linguistic-aware-error.jsonld"}}