Real-time, human-centered conversational AI requires systems that handle spoken dialogue with overlap and rapid turn-taking. Although full-duplex models promise these capabilities, empirical work applying them to conversational AI is still nascent. To fill this gap, this study investigates whether the full-duplex model can reproduce the human dialogue features. We adapt a full-duplex spoken dialogue model to a large corpus of second-language (L2) learner interviews and train proficiency-conditioned models. We then conduct real-time interview sessions between these models and a spoken dialogue system designed to elicit spontaneous learner speech, and analyze reaction time, response frequency, and fluency metrics across aggregated CEFR levels (A/B/C). Our results show that proficiency-conditioned models partially reproduce levelwise trends and distributions observed in human interviews across multiple metrics. These findings suggest that full-duplex models can reproduce dialogue features of human dialogues and offer a promising foundation for conversational AI systems.

Voice Activity Projection (VAP) models predict upcoming voice activities on a continuous timescale, enabling more nuanced turn-taking behaviors in spoken dialogue systems. Although previous studies have shown robust performance with audio-based VAP, the potential of incorporating additional physiological information, such as respiration, remains relatively unexplored. In this paper, we investigate whether respiratory information can enhance VAP performance in turn-taking. To this end, we collected Japanese dialogue data with synchronized audio and respiratory waveforms, and then we integrated the respiratory information into the VAP model. Our results showed that the VAP model combining audio and respiratory information had better performance than the audio-only model. This finding underscores the potential for improving the turn-taking performance of VAP by incorporating respiration.

This paper presents the development and capabilities of a spoken dialogue robot that uses respiration to enhance human-robot dialogue. By employing a respiratory estimation technique that uses video input, the dialogue robot captures user respiratory information during dialogue. This information is then used to prevent speech collisions between the user and the robot and to present synchronized pseudo-respiration with the user, thereby enhancing the smoothness and engagement of human-robot dialogue.