REVISITING SEMI-CONTINUOUS HIDDEN MARKOVMODELS

In the past decade, semi-continuous hidden Markov models (SC-HMMs) have not attracted much attention in the speech recognition community. Growing amounts of training data and increasing so-phistication of model estimation led to the impression that continu-ous HMMs are the best choice of acoustic model. However, recent work on recognition of under-resourced languages faces the same old problem of estimating a large number of parameters from lim-ited amounts of transcribed speech. This has led to a renewed in-terest in methods of reducing the number of parameters while main-taining or extending the modeling capabilities of continuous mod-els. In this work, we compare classic and multiple-codebook semi-continuous models using diagonal and full covariance matrices with continuous HMMs and subspace Gaussian mixture models. Experi-ments on the RM and WSJ corpora show that while a classical semi-continuous system does not perform as well as a continuous one, multiple-codebook semi-continuous systems can perform better, par-ticular when using full-covariance Gaussians. Index Terms — automatic speech recognition, acoustic model-ing 1