1 citations · 1 across the 5 of their papers we have counts for
5 papers
Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based metrics correlate better wit…
Evaluation of Automatic Speech Recognition Using Generative Large Language Models
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil +6
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 corr…
A Comprehensive Analysis of Tokenization and Self-Supervised Learning in End-to-End Automatic Speech Recognition applied on French Language
Thibault Bañeras-Roux, Mickael Rouvier, Jane Wottawa +1
The performance of end-to-end automatic speech recognition (ASR) systems enables their increasing integration into numerous applications. While there are various benefits to such s…
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…
A Benchmark of French ASR Systems Based on Error Severity
Antoine Tholly, Jane Wottawa, Mickael Rouvier +1
Automatic Speech Recognition (ASR) transcription errors are commonly assessed using metrics that compare them with a reference transcription, such as Word Error Rate (WER), which m…