@inproceedings{abe-etal-2026-effects,
title = "Effects of Dialogue Corpora Properties on Fine-Tuning a Moshi-Based Spoken Dialogue Model",
author = "Abe, Yuto and
Saeki, Mao and
Ohashi, Atsumoto and
Takamichi, Shinnosuke and
Fujie, Shiyna and
Kobayashi, Tetsunori and
Ogawa, Tetsuji and
Higashinaka, Ryuichiro",
editor = "Riccardi, Giuseppe and
Mousavi, Seyed Mahed and
Torres, Maria Ines and
Yoshino, Koichiro and
Callejas, Zoraida and
Chowdhury, Shammur Absar and
Chen, Yun-Nung and
Bechet, Frederic and
Gustafson, Joakim and
Damnati, G{\'e}raldine and
Papangelis, Alex and
D{'}Haro, Luis Fernando and
Mendon{\c{c}}a, John and
Bernardi, Raffaella and
Hakkani-Tur, Dilek and
Di Fabbrizio, Giuseppe {''}Pino{''} and
Kawahara, Tatsuya and
Alam, Firoj and
Tur, Gokhan and
Johnston, Michael",
booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
month = feb,
year = "2026",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwsds-1.10/",
pages = "104--108",
abstract = "This study investigates how interactional characteristics of spoken dialogue corpora influence the learning process and resulting behavior of speech language models for full-duplex dialogue systems. While previous research has mainly focused on improving acoustic and linguistic quality, an effective dialogue system must also capture and reproduce task-dependent interactional dynamics such as conversational tempo and turn-taking patterns. To analyze these properties, we evaluated multiple dialogue corpora using {NISQA} for speech quality, {LLM}-as-a-Judge for linguistic and semantic appropriateness, and four timing-based indicators: inter-pausal units, pause, gap, and overlap. A curriculum learning strategy was applied to fine-tune a Moshi-based full-duplex dialogue model by incrementally combining corpora with different interactional characteristics. Experimental results on a dialogue continuation task showed that corpus-specific interactional patterns effectively shape model behavior. Chat-style corpora facilitated natural rhythms with moderate overlaps and gaps, whereas consultation-style corpora promoted more stable and deliberate timing. Fine-tuning with high-quality audio improved speech quality, while using task-mismatched data degraded linguistic coherence."
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%0 Conference Proceedings
%T Effects of Dialogue Corpora Properties on Fine-Tuning a Moshi-Based Spoken Dialogue Model
%A Abe, Yuto
%A Saeki, Mao
%A Ohashi, Atsumoto
%A Takamichi, Shinnosuke
%A Fujie, Shiyna
%A Kobayashi, Tetsunori
%A Ogawa, Tetsuji
%A Higashinaka, Ryuichiro
%Y Riccardi, Giuseppe
%Y Mousavi, Seyed Mahed
%Y Torres, Maria Ines
%Y Yoshino, Koichiro
%Y Callejas, Zoraida
%Y Chowdhury, Shammur Absar
%Y Chen, Yun-Nung
%Y Bechet, Frederic
%Y Gustafson, Joakim
%Y Damnati, Géraldine
%Y Papangelis, Alex
%Y D’Haro, Luis Fernando
%Y Mendonça, John
%Y Bernardi, Raffaella
%Y Hakkani-Tur, Dilek
%Y Di Fabbrizio, Giuseppe ”Pino”
%Y Kawahara, Tatsuya
%Y Alam, Firoj
%Y Tur, Gokhan
%Y Johnston, Michael
%S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
%D 2026
%8 February
%I Association for Computational Linguistics
%C Trento, Italy
%F abe-etal-2026-effects
%X This study investigates how interactional characteristics of spoken dialogue corpora influence the learning process and resulting behavior of speech language models for full-duplex dialogue systems. While previous research has mainly focused on improving acoustic and linguistic quality, an effective dialogue system must also capture and reproduce task-dependent interactional dynamics such as conversational tempo and turn-taking patterns. To analyze these properties, we evaluated multiple dialogue corpora using NISQA for speech quality, LLM-as-a-Judge for linguistic and semantic appropriateness, and four timing-based indicators: inter-pausal units, pause, gap, and overlap. A curriculum learning strategy was applied to fine-tune a Moshi-based full-duplex dialogue model by incrementally combining corpora with different interactional characteristics. Experimental results on a dialogue continuation task showed that corpus-specific interactional patterns effectively shape model behavior. Chat-style corpora facilitated natural rhythms with moderate overlaps and gaps, whereas consultation-style corpora promoted more stable and deliberate timing. Fine-tuning with high-quality audio improved speech quality, while using task-mismatched data degraded linguistic coherence.
%U https://aclanthology.org/2026.iwsds-1.10/
%P 104-108
Markdown (Informal)
[Effects of Dialogue Corpora Properties on Fine-Tuning a Moshi-Based Spoken Dialogue Model](https://aclanthology.org/2026.iwsds-1.10/) (Abe et al., IWSDS 2026)
ACL
- Yuto Abe, Mao Saeki, Atsumoto Ohashi, Shinnosuke Takamichi, Shiyna Fujie, Tetsunori Kobayashi, Tetsuji Ogawa, and Ryuichiro Higashinaka. 2026. Effects of Dialogue Corpora Properties on Fine-Tuning a Moshi-Based Spoken Dialogue Model. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 104–108, Trento, Italy. Association for Computational Linguistics.