Robust Speaker-Independent Hidden Markov Model Based Word Spotter

Since January 1990, we have endeavoured, in conjunction with the Canadian Department of National Defense, to design a speaker-independent word-spotter. This paper describes the several aspects investigated over the past few months. The open-set nature of the problem requires different techniques than those used in continuous speech recognition. This paper presents the baseline word-spotting system, using hidden Markov models (HMM) to model both keyword and non-keyword speech. In the course of the research, we have found that the features and their transformations greatly affect the performance of the system. Since the task requires speech over a long period of time, various schemes were investigated to produce a robust system with respect to variations in background noise. We present the results of these investigations. Finally, experimental results from the investigations are presented.