Efficient corpus design for wake-word detection

Abstract

Wake-word detection is an indispensable technology for preventing virtual voice agents from being unintentionally triggered. Although various neural networks were proposed for wake-word detection, less attention has been paid to efficient corpus design, which we address in this study. For this purpose, we collected speech data via a crowdsourcing platform and evaluated the performance of several neural networks when different subsets of the corpus were used for training. The results reveal the following requirements for efficient corpus design to produce a lower misdetection rate: (1) short segments of continuous speech can be used as negative samples, but they are not as effective as random words; (2) utterances of "adversarial" words, i.e., phonetically similar words to a wake-word, contribute to improving performance significantly when they are used as negative samples; (3) it is preferable for individual speakers to provide both positive and negative samples; (4) increasing the number of speakers is better than increasing the number of repetitions of a wake-word by each speaker.