A 2-PASS HYBRID SYSTEM USING A LOW DIMENSIONAL AUDITORY MODEL FOR SPEAKER-INDEPENDENT ISOLATED-WORD RECOGNITION

Including speech knowledge in automatic speech recognition (ASR) systems is a good way to improve the performance of recognizers. In this paper, we propose the orion system which deals with speaker-independent ASR for isolated-words. orion is a two-pass hybrid system which uses several types of knowledge. This knowledge applies to psychoacoustics, physiology and phonetics. During the first pass, an auditory model, the perceptually-based linear prediction analysis (PLP), combines static and dynamic features to provide a set of parameters to the dynamic programming algorithm. After this stage 98% recognition accuracy was obtained for a digit vocabulary and 12 templates per word. In the case of a confusable vocabulary (E-SET), the introduction of phonetic knowledge in the second pass decreases the error rate by more than 60% (compared to the results of the first pass). © 1991.