Enhancing Automatic Speech Recognition of a Regional Dialect: A Pilot Study with Québécois French
Bibliographic record
Abstract
Automatic speech recognition (ASR) technologies have advanced in recent years, but performance still varies for underrepresented low-resource testing conditions. This proof-of-concept study examined the speech transcription performance of a state-of-the-art multilingual ASR system, Whisper, for Québécois French (QF). Due to the low-resource nature of QF, we developed a semi-automated pipeline for creating machine-learning-ready, well-aligned speech-text corpora from the Web. We created a data set of 2.5 hours from in total 83 speakers, covering various common topics. Using this data set, we measured the zero-shot Word Error Rate (WER) of Whisper’s base-size model (74M parameters) and small-size model (244M parameters). We found substantial performance gaps between its QF zero-shot WER and its published French WER on standardized benchmarks, motivating us to fine-tune Whisper for QF. Whisper was trained with the large-scale weak supervision method, and it has been reported to have high robustness, suitable as a pre-trained model. We maintained all the pre-trained weights in the encoder blocks and only unfroze the decoder blocks for retraining, resulting in 52M trainable parameters for the base-size model and 153M trainable parameters for the small-size model. A reduction of WER was seen after fine-tuning for both sizes; the fine-tuned small-size model achieved an average WER of 19.5%, approaching Whisper’s performance on the standard, well-represented French dialect. Our study showcased a promising initial approach in leveraging Whisper as a pre-trained model for targeted adaptive applications, particularly in the context of regional dialects, even with limited resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".