Generative factor analyzed HMM for automatic speech recognition

We show through a set of experiments to verify the potential of the GFA-HMM as an alternative acoustic modeling technique. In one experiment, by varying the latent dimension and the number of mixture components in the latent spaces, the GFA-HMM attained more compact representation than the standard HMM. In other experiments with varies noise types and speaking styles, the GFA-HMM was able to have (statistically significant) improvement with respect to the standard HMM.