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Autori principali: Serrand, Coralie, Boulianne, Gilles, Morsli, Amira
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2508.21193
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author Serrand, Coralie
Boulianne, Gilles
Morsli, Amira
author_facet Serrand, Coralie
Boulianne, Gilles
Morsli, Amira
contents We evaluate the performance of large pretrained multilingual speech recognition models on a regional variety of French spoken in Québec, Canada, in terms of speed, word error rate and semantic accuracy. To this end we build a benchmark and evaluation pipeline based on the CommissionsQc datasets, a corpus of spontaneous conversations recorded during public inquiries recently held in Québec. Published results for these models on well-known benchmarks such as FLEURS or CommonVoice are not good predictors of the performance we observe on CommissionsQC. Our results should be of interest for practitioners interested in building speech applications for realistic conditions or regional language varieties.
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spellingShingle Benchmarking Large Pretrained Multilingual Models on Québec French Speech Recognition
Serrand, Coralie
Boulianne, Gilles
Morsli, Amira
Audio and Speech Processing
We evaluate the performance of large pretrained multilingual speech recognition models on a regional variety of French spoken in Québec, Canada, in terms of speed, word error rate and semantic accuracy. To this end we build a benchmark and evaluation pipeline based on the CommissionsQc datasets, a corpus of spontaneous conversations recorded during public inquiries recently held in Québec. Published results for these models on well-known benchmarks such as FLEURS or CommonVoice are not good predictors of the performance we observe on CommissionsQC. Our results should be of interest for practitioners interested in building speech applications for realistic conditions or regional language varieties.
title Benchmarking Large Pretrained Multilingual Models on Québec French Speech Recognition
topic Audio and Speech Processing
url https://arxiv.org/abs/2508.21193