Insertion reduction in speech segmentation using neural network

Abstract

Statistical approach with non-fixed overlapping window size is able to make good identification of discontinuity in speech signal without further knowledge upon the phonetic sequence. This however, leads to increase number of insertion and thus increase confusion in recognition. This paper present a fusion between statistical and connectionist approach namely divergence algorithm and MLP neural network to improved segmentation by reducing insertions. The experiment conducted on Malay semi-spontaneous connected digit in classroom environment. The digit strings were manually segmented and trained using neural network with three set of data. The first training set trained without silence patterns, the second include silence while the last set introduced both silence and false pattern in the training. The experimental result on digit string segmentation shows number of insertion reduction of more than 5 times in comparison using divergence alone with increment of accuracy up to 40%. The drawback however, the number of admission also increase to more than 10 times. Nevertheless, match segmentation rate still above 85%.