Performance of a speaker-independent continuous speech recognizer

Abstract

The speaker-independent word recognition component of a Speech Understanding System is described. It is based on Hidden Markov Models (HMM) of phonetic units and produces a lattice of word hypotheses suitable to be parsed by a linguistic analyzer that uses the acoustic scores of words for generating the best interpretation of the spoken utterance given a set of syntactic and semantic rules. This work presents the results of a set of experiments carried out with the aim of selecting a suitable set of sub-word unit models for an E-mail inquire application. The inquiries have been recorded over a dialed-up telephone line connected to the local PABX. The performance of the system is presented and comparative results are given for Discrete and Continuous Density HMMs.