  Voice Assistants aim to fulfill user requests by choosing the best intent
from multiple options generated by its Automated Speech Recognition and Natural
Language Understanding sub-systems. However, voice assistants do not always
produce the expected results. This can happen because voice assistants choose
from ambiguous intents - user-specific or domain-specific contextual
information reduces the ambiguity of the user request. Additionally the user
information-state can be leveraged to understand how relevant/executable a
specific intent is for a user request. In this work, we propose a novel
Energy-based model for the intent ranking task, where we learn an affinity
metric and model the trade-off between extracted meaning from speech utterances
and relevance/executability aspects of the intent. Furthermore we present a
Multisource Denoising Autoencoder based pretraining that is capable of learning
fused representations of data from multiple sources. We empirically show our
approach outperforms existing state of the art methods by reducing the
error-rate by 3.8%, which in turn reduces ambiguity and eliminates undesired
dead-ends leading to better user experience. Finally, we evaluate the
robustness of our algorithm on the intent ranking task and show our algorithm
improves the robustness by 33.3%.
