Optimizing recognition and rejection performance in wordspotting systems

Compares the performance which can be achieved by different hidden Markov model (HMM) based wordspotting techniques when their parameters are tuned to optimize recognition and rejection rates. An alternative approach which does not attempt to explicitly model extraneous speech or non-speech noise is also proposed. After optimization of each of these approaches, it appears that the proposed version performs at least as well as the other methods with the advantage of simplicity and possibility to be used in hybrid models using HMMs with a multilayer perceptron (MLP). Test results are reported on a speaker independent telephone database containing 10 keywords as well as on the speaker independent ARPA resource management database in which between 10 and 250 keywords were defined