This paper describes a novel method of live keyword spotting using a
two-stage time delay neural network. The model is trained using transfer
learning: initial training with phone targets from a large speech corpus is
followed by training with keyword targets from a smaller data set. The accuracy
of the system is evaluated on two separate tasks. The first is the freely
available Google Speech Commands dataset. The second is an in-house task
specifically developed for keyword spotting. The results show significant
improvements in false accept and false reject rates in both clean and noisy
environments when compared with previously known techniques. Furthermore, we
investigate various techniques to reduce computation in terms of
multiplications per second of audio. Compared to recently published work, the
proposed system provides up to 89% savings on computational complexity.

本文提出了一种利用两阶段时延神经网络进行实时关键词识别的新方法。该模型使用迁移学习进行训练，并在谷歌语音命令数据集和自行开发的关键字识别任务上进行了评估。与以往技术相比，本方法在干净和嘈杂的环境中均显著改善了误接受和误拒绝率，并采用了各种技术来减少计算。此外，与最近发表的研究相比，本文所提出的系统在音频的每秒乘法计算上提供了高达 89％的节省。