We propose a method to reduce false voice triggers of a speech-enabled personal assistant by post-processing the hypothesis lattice of a server-side large-vocabulary continuous speech recognizer (LVCSR) via a neural network. We first discuss how an estimate of the posterior probability of the trigger phrase can be obtained from the hypothesis lattice using known techniques to perform detection, then investigate a statistical model that processes the lattice in a more explicitly data-driven, discriminative manner. We propose using a Bidirectional Lattice Recurrent Neural Network (LatticeRNN) for the task, and show that it can significantly improve detection accuracy over using the 1-best result or the posterior.
@article{arxiv.2003.00304,
title = {Voice trigger detection from LVCSR hypothesis lattices using bidirectional lattice recurrent neural networks},
author = {Woojay Jeon and Leo Liu and Henry Mason},
journal= {arXiv preprint arXiv:2003.00304},
year = {2020}
}