Latent perceptual mapping: a new acoustic modeling framework for speech recognition

While hidden Markov modeling is still the dominant paradigm for speech recognition, in recent years there has been renewed interest in alternative, template-like approaches to acoustic modeling. Such methods sidestep usual HMM limitations as well as inherent issues with parametric statistical distributions, though typically at the expense of large amounts of memory and computing power. This paper introduces a new framework, dubbed latent perceptual mapping, which naturally leverages a reduced dimensionality description of the observations. This allows for a viable parsimonious template-like solution where models are closely aligned with perceived acoustic events. Context-independent phoneme classification experiments conducted on the TIMIT database suggest that latent perceptual mapping achieves results comparable to conventional acoustic modeling but at potentially significant savings in online costs.