Accurate Detection of Wake Word Start and End Using a CNN

Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \textit{wake word} as it is used to wake up voice assistant enabled devices. Together with wake word detection, accurate estimation of wake word endpoints (start and end) is an important task of KWS. In this paper, we propose two new methods for detecting the endpoints of wake words in neural KWS that use single-stage word-level neural networks. Our results show that the new techniques give superior accuracy for detecting wake words' endpoints of up to 50 msec standard error versus human annotations, on par with the conventional Acoustic Model plus HMM forced alignment. To our knowledge, this is the first study of wake word endpoints detection methods for single-stage neural KWS.Comment: Proceedings of INTERSPEEC

Unsupervised Pre-Training for Voice Activation

The problem of voice activation is to find a pre-defined word in the audio stream. Solutions such as keyword spotter “Ok, Google” for Android devices or keyword spotter “Alexa” for Amazon devices use tens of thousands to millions of keyword examples in training. In this paper, we explore the possibility of using pre-trained audio features to build voice activation with a small number of keyword examples. The contribution of this article consists of two parts. First, we investigate the dependence of the quality of the voice activation system on the number of examples in training for English and Russian and show that the use of pre-trained audio features, such as wav2vec, increases the accuracy of the system by up to 10% if only seven examples are available for each keyword during training. At the same time, the benefits of such features become less and disappear as the dataset size increases. Secondly, we prepare and provide for general use a dataset for training and testing voice activation for the Lithuanian language. We also provide training results on this dataset.This article belongs to the Section Computing and Artificial Intelligenc