An Application of Recurrent Neural Networks to Discriminative Keyword Spotting

Keyword spotting is a detection task consisting in discovering the presence of specific spoken words in unconstrained speech. The majority of keyword spotting systems are based on generative hidden Markov models and lack discriminative capabilities. However, discriminative keyword spotting systems are based on the estimation of a posteriori probabilities at the frame-level, hence they make use of information from short time spans. This paper presents a discriminative keyword spotting system based on recurrent neural networks only, that uses information from long time spans to estimate keyword probabilities. In a keyword spotting task in a large database of unconstrained speech where an HMM-based speech recogniser achieves a word accuracy of only 65 %, the system achieved a keyword spotting accuracy of 84.5 %.