Syllable-based connected spoken word recognition by two pass O(n) DP matching and hidden Markov models

In this paper, we present an approach to isolated and connected word recognition by using dynamic time warping algorithm which referes as a hidden Markov model. The classification consists of computing the a posteriori probability for each word model and choosing the word model that gives the highest probability. The probability is calculated by two different ways: One is the exact algorithm and the other is the approximate (Viterbi) algorithm. In our system, first, an input speech is recognized as a string of monosyllables by the syllable-based O(n) DP matching. Second, the recognized string is matched with a mono-syllable string of each lexical model, and the word or word sequence with the highest probability is recognized as the input speech by using O(n) DP matching based on a hidden Markov model. Reference patterns consist of 68 mono-syllables, and test patterns consists of 90 isolated words, two connected words and three connected words. We conclude from the results of the experiments that: (1) The results by using 3 candidates are much better than those by using only best candidate for each segment. (2) The approximate algorithm has almost the same performance as the exact algorithm. (3) The extended algorithm for connected word recognition works well.