Impact of High-to-Low Resource Speech Data Ratio on Few-Shot ASR Convergence in Unseen Languages

Abstract

Abstract: Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art performance in various tasks. However, their applicability is still less explored in low-resource settings. This work investigates the use of Speech LLMs for low-resource Automatic Speech Recognition using the SLAM-ASR framework, where a trainable lightweight projector connects a speech encoder and a LLM. Firstly, we assess training data volume requirements to match Whisper-only performance, re-emphasizing the challenges of limited data. Secondly, we show th

Research Question

How does the ratio of high-resource to low-resource speech data in SLAM-ASR pretraining impact the few-shot word error rate convergence for unseen low-resource languages?

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