A hybrid wordspotting method for spontaneous speech understanding using word-based pattern matching and phoneme-based HMM

We have proposed a new wordspotting method, combining word-based pattern matching and phoneme-based hidden Markov model (HMM). Word-based pattern matching based on the time-frequency representation of a whole word pattern is robust against pattern variations and background noise, while the phoneme-based HMM, which represents phonemic features within a word pattern, is flexible for expanding the vocabulary. Because of the difference in scope, these two have their own characteristics in terms of robustness and accuracy. To take advantage of the features of these two, we have integrated these different types of wordspotting results under a unified criterion. A syntactic and semantic parser is also utilized to prune the wordspotting results for spontaneous speech understanding. Experimental results indicate the effectiveness of the proposed method