A whole word recurrent neural network for keyword spotting

Abstract

The authors present a neural network which is trained on word examples to perform the wordspotting task. This network has multiple recurrent connections with time delay to account for temporal dynamics. A single network may be trained to recognize one word or many words. A hybrid wordspotter is evaluated in which a conventional wordspotter (based on dynamic time warping word matching) is used to screen incoming speech for potential keywords which are then passed to the network for the final accept/reject decision. Initial tests on a standard wordspotting test corpora resulted in improved keyword recognition at false alarm rates above zero.<>