Training and search algorithms for an interactive wordspotting system

Algorithms for a speaker-dependent wordspotting system based on hidden Markov models (HMMs) are described. The system allows a user to specify keywords dynamically and to train the associated HMMs via a single repetition of a keyword. Nonkeyword speech is modeled using an HMM trained from a prerecorded sample of continuous speech. The wordspotter is intended for interactive applications, such as the editing of voice mail or mixed-media documents, and for keyword indexing in audio or video recordings. The forward-backward search algorithm used in the wordspotter is compared with the Viterbi decoder on the basis of speed and accuracy. In addition, an algorithm for speaker adaptation is described which allows indexing by a user into another talker´s speech