The study investigates a multilingual approach to predicting turn-taking in spoken dialogues using voice activity projection. It compares monolingual and multilingual models across English, Mandarin, and Japanese datasets. The results show that while monolingual models struggle with cross-lingual predictions, a multilingual model performs well across all languages. The study also delves into the sensitivity to pitch cues for turn-taking and evaluates different audio encoders' impact on model performance.
The research addresses key challenges in modeling turn-taking behavior in spoken interactions, emphasizing the need for language-specific training and understanding prosodic cues. By leveraging a multilingual approach, the study aims to enhance the accuracy and adaptability of predictive models in diverse linguistic contexts.
Key findings include the successful application of a multilingual VAP model for turn-taking prediction, showcasing its ability to identify language differences and predict speaker transitions effectively. The study also highlights the significance of pitch information in certain languages like Mandarin and Japanese for accurate turn prediction.
Overall, the research contributes valuable insights into improving spoken dialogue systems' naturalness and efficiency by incorporating multilingual models with enhanced sensitivity to linguistic nuances.
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