Application of a sequential pattern learning system to connected speech recognition

An Experimental Learning Element (ELE) for learning and recognizing sequential patterns is being developed as an adaptable pattern classifier of a larger learning system. Once external patterns are converted into a linear sequence of named objects, the ELE can build models that associate input object sequences with expected output state sequences. The ELE has been successfully demonstrated in learning and recognizing hand-printed characters. This paper describes the ELE and compares its performance with a Dynamic Time Wrap (DTW) based speech recognition system on the task of connected digit recognition. If permitted to continually learn the ELE reaches the same performance level as the DTW-CSR on the same quantized speech test data.