Agentic Data Sampling Framework for Multilingual ASR via Linguistic-Aware Error-Driven Optimization

Overview

arXiv:2610.36538v1 Announce Type: new Abstract: 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

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