{"paper":{"title":"Benchmarking Large Pretrained Multilingual Models on Qu\\'ebec French Speech Recognition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Amira Morsli, Coralie Serrand, Gilles Boulianne","submitted_at":"2025-08-28T20:17:26Z","abstract_excerpt":"We evaluate the performance of large pretrained multilingual speech recognition models on a regional variety of French spoken in Qu\\'ebec, 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 Qu\\'ebec. 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 pra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.21193","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.21193/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}