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This project provides a dataset for exploring the timing of conversational turns, specifically focusing on identifying and predicting Transition Relevance Places (TRPs). TRPs are points during speech where a listener could potentially take over the conversation. This dataset is particularly useful for improving turn-taking capabilities in spoken dialogue systems, which are crucial for developing artificial agents capable of human-like interaction. The dataset includes human responses to naturally occurring conversational turns and is designed to study both turn-end and within-turn TRPs. Note that this dataset is released as part of the paper Large Language Models Know What to Say But Not When To Speak, published in the findings of EMNLP 2024.
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