  Recent TTS systems are able to generate prosodically varied and realistic
speech. However, it is unclear how this prosodic variation contributes to the
perception of speakers' emotional states. Here we use the recent psychological
paradigm 'Gibbs Sampling with People' to search the prosodic latent space in a
trained GST Tacotron model to explore prototypes of emotional prosody.
Participants are recruited online and collectively manipulate the latent space
of the generative speech model in a sequentially adaptive way so that the
stimulus presented to one group of participants is determined by the response
of the previous groups. We demonstrate that (1) particular regions of the
model's latent space are reliably associated with particular emotions, (2) the
resulting emotional prototypes are well-recognized by a separate group of human
raters, and (3) these emotional prototypes can be effectively transferred to
new sentences. Collectively, these experiments demonstrate a novel approach to
the understanding of emotional speech by providing a tool to explore the
relation between the latent space of generative models and human semantics.

Human-Computer Interaction;Machine Learning