An application of recurrent neural networks to discriminative keyword spotting

Santiago Fernández, Alex Graves, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

164 Scopus citations

Abstract

The goal of keyword spotting is to detect 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 currently based on frame-level posterior probabilities of sub-word units. This paper presents a discriminative keyword spotting system based on recurrent neural networks only, that uses information from long time spans to estimate word-level posterior probabilities. In a keyword spotting task on a large database of unconstrained speech the system achieved a keyword spotting accuracy of 84.5 %. © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages220-229
Number of pages10
ISBN (Print)9783540746935
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

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