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Voice controlled virtual assistants (VAs) are now available in smartphones,cars, and standalone devices in homes. In most cases, the user needs to first"wake-up" the VA by saying a particular word/phrase every time he or she wantsthe VA to do something. Eliminating the need for saying the wake-up word forevery interaction could improve the user experience. This would require the VAto have the capability to detect the speech that is being directed at it andrespond accordingly. In other words, the challenge is to distinguish betweensystem-directed and non-system-directed speech utterances. In this paper, wepresent a number of neural network architectures for tackling thisclassification problem based on using only acoustic features. Thesearchitectures are based on using convolutional, recurrent and feed-forwardlayers. In addition, we investigate the use of an attention mechanism appliedto the output of the convolutional and the recurrent layers. It is shown thatincorporating the proposed attention mechanism into the models always leads tosignificant improvement in classification accuracy. The best model achievedequal error rates of 16.25 and 15.62 percents on two distinct realisticdatasets.

Exploring attention mechanism for acoustic-based classification of speech utterances into system-directed and non-system-directed