Phonetic segmentation using statistical correction and multi-resolution fusion

This paper focuses on the generation of accurate phonetic segmentations. Statistical methods based on absolute and relative correction are discussed and experimented on both monophone and biphone models to improve the segmentation results. The influence of search range on the statistical correction process is studied and a state selection technique is used to enhance the correction results. This paper also explores the influence of resolution (stepsize) of HMMs and proposes a multi-resolution fusion process to further refine the statistically corrected results. Improvements of segmentation results in terms of segmentation accuracy, mean absolute error (MAE), and root mean square error (RMSE) can be observed by applying the proposed refinement methods.