The importance of segmentation probability in segment based speech recognizers

In segment based recognizers, variable length speech segments are mapped to the basic speech units (phones, diphones, ...). We address the acoustical modeling of these basic units in the framework of segmental posterior distribution models (SPDM). The joint posterior probability of a unit sequence u_ and a segmentation s_, Pr(u_,s_|X_) can be written as the product of the segmentation probability Pr(s_|X_) and the unit classification probability Pr(u_|s_,X_), where X_ is the sequence of acoustic observation parameter vectors. In particular, we point out the role of the segmentation probability and demonstrate that it does improve the recognition accuracy. We present evidence for this in two different tasks (speaker dependent continuous word recognition in French and speaker independent phone recognition in American English) in combination with two different unit classification models