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.

通过神经网络对服务端大词汇连续语音识别器的假触发词假设网络进行后处理，从而通过检测显著提高检测准确性。

使用双向格子递归神经网络从 LVCSR 假设格中检测语音触发器

Voice trigger detection from LVCSR hypothesis lattices using  bidirectional lattice recurrent neural networks

This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we have found useful in training these networks. We evaluate our system
on the commonly used NIST 2000 conversational telephony test set, and
significantly exceed the previously published performance of similar systems,
both with and without the use of an external language model and decoding
technology.

本文提出了一种基于 CTC 的全神经网络语音识别器的设计方法，包括新的符号存储库、基于迭代的 CTC 方法、稳定化方法和初始化方法等，使用 NIST 2000 会话电话测试集进行了评估，结果表明该系统明显超过了先前发布的相似系统的性能，无需使用外部语言模型和解码技术。