A segmentation algorithm for connected word recognition based on estimation principles

Recognition of connected words can be performed by segmenting the word string automatically into single-word components which are then classified by a single-word recognition system. We propose and investigate a speaker-independent segmentation procedure which is based completely on statistical principles. An estimation algorithm, adapted to the statistical data of the signal parameters, determines the word boundaries. The statistical data are computed from vocabulary-dependent speech samples of different speakers. The segmentation procedure, which operates independently of the single-word recognizer, has been tested with connected digits. The results show that an estimation algorithm based on quadratic polynomials yields a very reliable segmentation. The segmentation procedure is also well suited for a speech input where the number of words in a word string is not known to the recognition system. Based on the above segmentation procedure, we have carried out several recognition experiments on two-to-four-digit strings. The investigations show that the proposed segmentation algorithm provides an efficient tool to tackle the effects of coarticulation between adjacent words. We present a training procedure which automatically adapts the classifier to the speaker-dependent effects of coarticulation.