Auto-imputing radial basis functions for neural-network turn-taking models

A stochastic turn-taking (STT) model is a per-frame predictor of incipient speech activity. Its ability to make predictions at any instant in time makes it particularly well-suited to the analysis and synthesis of interactive conversation. At the current time, however, STT models are limited by their inability to accept features which may frequently be undefined. Rather than attempting to impute such features, this work proposes and evaluates a mechanism which implicitly conditions Gaussiandistributed features on Bernoulli-distributed indicator features, making prior imputation unnecessary. Experiments indicate that the proposed mechanisms achieve predictive parity with standard model structures, while at the same time offering more direct interpretability and the desired insensitivity to missing feature values.