Impact of LLM Selection in SLAM-ASR on Low- versus High-Resource Speech Recognition Performance on the MIRACL Benchmark

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 choice of LLM (e.g., Falcon, Llama, or Mistral) in the SLAM-ASR framework affect the downstream speech recognition performance in low-resource languages compared to high-resource languages, as measured by WER on the MIRACL benchmark?

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