Maximizing the continuity in segmentation - A new approach to model, segment and recognize speech

This paper presents a new approach to speech modeling and recognition. The new approach consists of a statistical model to represent up to sentence-long temporal dynamics in the training data, and an algorithm to identify the matching segments with maximum continuities between the training and testing sentences. Recognition is performed by combining the longest matching segments found from the training sentences. Because of their richer and more distinct temporal dynamics, longer speech segments as whole units can be recognized with lower error rates than shorter speech segments. Therefore basing recognition on the longest matching segments optimizes the discrimination and hence recognition of speech. The new approach has been evaluated on the TIMIT database for identifying matching speech segments. The results obtained are encouraging given the very low parametric complexity of the new model.