Large-vocabulary spoken word recognition using simplified time-warping patterns

We propose a new matching algorithm for large vocabulary spoken word recognition, which gives a recognition score compatible to that of the traditional DP matching algorithm, but requires less than 1/10 as much calculation. By a computer simulation of 1,000 categories in speaker dependent recognition of speech samples uttered by five male adult speakers, an average recognition score of 95.8% was obtained. We have constructed a real-time speaker dependent speech recognizer using our algorithm. We are now examining the application of this recognizer to Japanese text input.