Region Proposal Network Based Small-Footprint Keyword Spotting

Abstract

We apply an anchor-based region proposal network (RPN) for end-to-end keyword spotting (KWS). RPNs have been widely used for object detection in image and video processing; here, it is used to jointly model keyword classification and localization. The method proposes several anchors as rough locations of the keyword in an utterance and jointly learns classification and transformation to the ground truth region for each positive anchor. Additionally, we extend the keyword/non-keyword binary classification to detect multiple keywords. We verify our proposed method on a hotword detection data set with two hotwords. At a false alarm rate of one per hour, our method achieved more than 15% relative reduction in false rejection of the two keywords over multiple recent baselines. In addition, our method predicts the location of the keyword with over 90% overlap, which can be important for many applications.