On the use of inter-word context-dependent units for word juncture modeling

Abstract

Word juncture coarticulation is one of the major sources of acoustic variability for initial and final word segments when spoken in fluent speech. One way to improve characterization of word pronuciations in continuous speech is to include inter-word contexts in lexical representations, similar to the way intra-word contexts are utilized. In this paper we investigate the issues related to the modeling of this set of inter-word, context-dependent units using continuous density hidden Markov models. Under such a modeling framework, it is usually required to have enough training tokens available for each unit in order reliably to estimate the parameters of the unit. Therefore, each context-dependent unit is included in the set of units to be modeled only when its frequency of occurrence in the training data exceeds a prescribed threshold. Testing such a unit selection and modeling strategy on the DARPA resource management task, it was found that the incorporation of inter-word units gave a 15–25% word error reduction compared to the base-line continuous speech recognition system using only intra-word units.