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cs.CL2026
A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition
Thibault Bañeras-Roux, Mickael Rouvier, Jane Wottawa +1
The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been heavily criticized for their p…
cs.CL2026★ 1 cited
HATS: An Open data set Integrating Human Perception Applied to the Evaluation of Automatic Speech Recognition Metrics
Thibault Bañeras Roux, Jane Wottawa, Mickael Rouvier +2
Conventionally, Automatic Speech Recognition (ASR) systems are evaluated on their ability to correctly recognize each word contained in a speech signal. In this context, the word e…
cs.CL2026★ 6 cited
Qualitative Evaluation of Language Model Rescoring in Automatic Speech Recognition
Thibault Bañeras-Roux, Mickaël Rouvier, Jane Wottawa +1
Evaluating automatic speech recognition (ASR) systems is a classical but difficult and still open problem, which often boils down to focusing only on the word error rate (WER). How…