view teaser image A smart Speech emotion recognition system that can tell how confident it is about its predictions Selective prediction models can be most useful in real-world applications. A model with smartness to abstain from prediction when in doubt can facilitate human-in-the-loop solutions Utilized Monte Carlo approximation to variational inference to implement create a smart emotion recognition system and demonstrated its benefits using reject options.

view teaser image Ensemble of students taught by probabilistic teachers reduces confusion among students and at the same time improves their performance as well. Speech emotion recognition system that gives scalable, reliable and consistent predictions, tailoring it to real-world applications Knowledge transfer between teacher and student using the learned latent emotional representations by the teachers Incorporating model diversity and preserving prediction uncertainty using Monte Carlo dropout and emsembling