arXiv subjects

Gilles Boulianne

Publications and source records attributed to Gilles Boulianne.

3 recordsLinked to original sources

arXiv2026

From Metrics to Natural Dialogue: French Full-Duplex Benchmark for Spoken Dialogue Models

Full-duplex spoken dialogue models aim to make voice agents more natural by allowing them to listen, speak, pause, and respond during ongoing conversation. However, it is not clear whether full-duplex benchmarks behave the same way when models are evaluated in a different language. To investigate this, we introduce a French full-duplex benchmark (FDB) with two variants, CALLFC-FDB for Canadian French and MEDIA-FDB for European French, and compare them with an English FDB. Built from real spoken resources, these benchmarks evaluate key full-duplex skills, including pause handling, turn-taking, backchannels, and interruptions. Beyond introducing French FDBs, we evaluate human--human conversations with FDB metrics to better understand the values these metrics take in real-world dialogue. Our analysis reveals that most timing-based metrics behave similarly across languages, while content-based evaluation degrades under language mismatch. We also find a trade-off between optimizing benchmark metrics and preserving conversational naturalness.

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arXiv2025

Towards Improved Speech Recognition through Optimized Synthetic Data Generation

Supervised training of speech recognition models requires access to transcribed audio data, which often is not possible due to confidentiality issues. Our approach to this problem is to generate synthetic audio from a text-only corpus using a state-of-the-art text-to-speech model with voice cloning capabilities. Our goal is to achieve automatic speech recognition (ASR) performance comparable to models trained on real data. We explore ways to optimize synthetic data generation through finetuning, filtering and evaluation, and its use for training an end-to-end encoder-decoder ASR model. Experiments were conducted using two datasets of spontaneous, conversational speech in Québec French. We show that improving data generation leads to large improvements in the final ASR system trained on synthetic data.

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arXiv2025

Benchmarking Large Pretrained Multilingual Models on Québec French Speech Recognition

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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