A framework for the automatic inference of stochastic turn-taking styles

Conversant-independent stochastic turntaking (STT) models generally benefit from additional training data. However, conversants are patently not identical in turn-taking style: recent research has shown that conversant-specific models can be used to refractively detect some conversants in unseen conversations. The current work explores an unsupervised framework for studying turn-taking style variability. First, within a verification framework using an information-theoretic model distance, sides cluster by conversant more often than not. Second, multi-dimensional scaling onto low-dimensional subspaces appears capable of preserving distance. These observations suggest that, for many speakers, turn-taking style as characterized by time-independent STT models is a stable attribute, which may be correlated with other stable speaker attributes such as personality. The exploratory techniques presented stand to benefit speaker diarization technology, dialogue agent design, and automated psychological diagnosis.