Lexical stress recognition using hidden Markov models

Abstract

A probabilistic algorithm is described for the estimation of the lexical stress pattern of English words from the acoustic signal using hidden Markov models (HMMs) with continuous asymmetric Gaussian probability density functions. Adopting a binary stressed-unstressed syllable classification strategy two five-state HMMs of the left-to-right type were generated, one for each stress value. Training observation vectors were extracted from a corpus of bisyllabic stress-minimal word pairs and consisted of nine acoustic measurements based on fundamental frequency, syllabic energy and coarse linear prediction spectra. Evaluation of both models using a set of recordings of the same word pairs yielded an average stress recognition rate of 94%. >