Explicit segmentation of speech using gaussian models

Abstract

The authors investigate an automatic method to segment labeled speech. The method needs an initial estimation of the segmentation which is provided by an alignment based on HMM. Afterwards, the boundaries are refined moving the frontier frames to the segment which is more similar to the speech frame. Gaussian PDFs are used as a similarity measure. The performance of the method is evaluated using the TIMIT database. If boundary deviations (from the reference position) larger than 20 ms are counted as errors, then the replacement of the boundaries reduces the error by 30%. Additional experiments show how the proposed method makes the performance independent of the speaker dependent or speaker independent data used to estimate the HMM.