This paper presents an approach to boundary estimation for automatic segmentation of speech given a phone (sound) sequence. The technique presented represents an extension to existing approaches to Hidden Markov Model based automatic segmentation which modifies the topology of the model to control for duration. An HMM system trained with this modified topology places 77.10%, 86.72% and 91.15% of the boundaries, on the TIMIT speech test corpus annotations, within 10, 15 and 20 ms respectively as compared with manual annotations. This represents an improvement over the baseline result of 70.99%, 83.50% and 89.18% for initial boundary estimation.
Bibliographic reference. Ogbureke, Kalu U. / Carson-Berndsen, Julie (2009): "Improving initial boundary estimation for HMM-based automatic phonetic segmentation", In INTERSPEECH-2009, 884-887.