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arXiv · 2506.00722

Chain-of-Thought Training for Open E2E Spoken Dialogue Systems

Siddhant Arora·Jinchuan Tian·Hayato Futami·Jee-weon Jung·Jiatong Shi·Yosuke Kashiwagi·Emiru Tsunoo·Shinji Watanabe
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Abstract

Unlike traditional cascaded pipelines, end-to-end (E2E) spoken dialogue systems preserve full differentiability and capture non-phonemic information, making them well-suited for modeling spoken interactions. However, existing E2E approaches often require large-scale training data and generates responses lacking semantic coherence. We propose a simple yet effective strategy leveraging a chain-of-thought (CoT) formulation, ensuring that training on conversational data remains closely aligned with the multimodal language model (LM)'s pre-training on speech recognition~(ASR), text-to-speech synthesis (TTS), and text LM tasks. Our method achieves over 1.5 ROUGE-1 improvement over the baseline, successfully training spoken dialogue systems on publicly available human-human conversation datasets, while being compute-efficient enough to train on just 300 hours of public human-human conversation data, such as the Switchboard. We will publicly release our models and training code.

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Siddhant Arora, Jinchuan Tian, Hayato Futami, Jee-weon Jung, Jiatong Shi, Yosuke Kashiwagi, Emiru Tsunoo, Shinji Watanabe. 2025-05-31. Chain-of-Thought Training for Open E2E Spoken Dialogue Systems. https://arxiv.org/abs/2506.00722

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