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            <namePart type="family">Atanasova</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T From Metrics to Natural Dialogue: French Full-Duplex Benchmark for Spoken Dialogue Models
%A Soltani, Hamid
%A Boulianne, Gilles
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F soltani-boulianne-2026-metrics
%X 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.
%U https://aclanthology.org/2026.emnlp-industry.137/
%P 2302-2310

Markdown (Informal)

[From Metrics to Natural Dialogue: French Full-Duplex Benchmark for Spoken Dialogue Models](https://aclanthology.org/2026.emnlp-industry.137/) (Soltani & Boulianne, EMNLP 2026)

• From Metrics to Natural Dialogue: French Full-Duplex Benchmark for Spoken Dialogue Models (Soltani & Boulianne, EMNLP 2026)

ACL

• Hamid Soltani and Gilles Boulianne. 2026. From Metrics to Natural Dialogue: French Full-Duplex Benchmark for Spoken Dialogue Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2302–2310, Budapest, Hungary. Association for Computational Linguistics.