A speaker independent recognition algorithm for connected word using word boundary hypothesizer

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Abstract
A speaker independent recognition algorithm for connected words is described which uses a word boundary hypothesizer to reduce computational cost, as well as a robust word classifier and an effective scoring strategy. The word boundary hypothesizer predicts possible candidates for word boundaries at a variable rate which is controlled by a difference in adjacent frame spectra, obtained by bandpass filters. It reduces computational cost of the algorithm to about one-tenth, compared with a conventional approach. The word classifier uses a statistical pattern recognition technique to calculate word similarities and discriminate a word for a provisional interval between hypothesized boundaries. As the scoring strategy for evaluating possible word strings, word scores are calculated and accumulated for continuous intervals. A word score is calculated from a word similarity by an equation which models the a posteriori probability of correct word position. An experiment was performed for 35 four-connected digits uttered by ten male speakers. The string recognition rate was 93.9% (word rate = 98.4%). It was also shown that the algorithm is superior to a method which regularly skips some frames for boundary hypothesis.

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