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This paper analyzes synchronization and turn-taking dynamics in full-duplex speech dialogue models by simulating conversations between two instances of the Moshi model, measuring representational alignment via CKA and predicting turn boundaries with LSTM probes.
When2Speak is a synthetic dataset and pipeline for training LLMs to decide when to speak in multi-party conversations. Fine-tuning on this dataset significantly improves turn-taking, with reinforcement learning reducing missed interventions from 50% to ~20%.