Factor analysed hidden Markov models

This paper presents a general form of acoustic model for speech recognition. The model is based on an extension to factor analysis where the low dimensional subspace is modelled with a mixture of Gaussians hidden Markov model (HMM) and the observation noise by a Gaussian mixture model. Here the HMM output vectors are the latent variables of a general factor analyser. The model combines shared factor analysis with a dynamic version of independent factor analysis. This factor analysed HMM (FAHMM) provides an alternative, compact, model to handle intra-frame correlation. Furthermore, it allows variable dimension subspaces to be explored. A variety of model configurations and sharing schemes are examined, some of which correspond to standard systems. The training and recognition algorithms for FAHMMs are described and some initial result with Switchboard are presented.