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arXiv · 2604.21928

Evaluation of Automatic Speech Recognition Using Generative Large Language Models

Thibault Bañeras-Roux·Shashi Kumar·Driss Khalil·Sergio Burdisso·Petr Motlicek·Shiran Liu·Mickael Rouvier·Jane Wottawa·Richard Dufour
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Abstract

Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based semantic metrics are better correlated with human perception, but decoder-based Large Language Models (LLMs) remain underexplored for this task. This paper evaluates their relevance through three approaches: (1) selecting the best hypothesis between two candidates, (2) computing semantic distance using generative embeddings, and (3) qualitative classification of errors. On the HATS dataset, the best LLMs achieve 92--94\% agreement with human annotators for hypothesis selection, compared to 63\% for WER, also outperforming semantic metrics. Embeddings from decoder-based LLMs show performance comparable to encoder models. Finally, LLMs offer a promising direction for interpretable and semantic ASR evaluation.

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Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil, Sergio Burdisso, Petr Motlicek, Shiran Liu, Mickael Rouvier, Jane Wottawa, Richard Dufour. 2026-04-23. Evaluation of Automatic Speech Recognition Using Generative Large Language Models. https://arxiv.org/abs/2604.21928

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