Improvements and applications for key word recognition using hidden Markov modeling techniques

Abstract

A hidden Markov model based key wordspotting algorithm developed previously can recognize key words from a predefined vocabulary list spoken in an unconstrained fashion. Improvements in the feature analysis used to represent the speech signal and modeling techniques used to train the system are explored. The authors discuss several task domain issues which influence evaluation criteria. They present results from extensive evaluations on three speaker independent databases: the 20 word vocabulary Stonehenge Road Rally database, distributed by the National Security Agency, a five word vocabulary used to automate operator-assisted calls, and a three word Spanish vocabulary that is currently being tested in Spain's telephone network. Currently, recognition accuracies range from 99.9% on the Spanish database to 74% (with 8.8 FA/H/W) on the Stonehenge task.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>