Factorial HMMs for acoustic modeling

In the machine learning research field several extensions of hidden Markov models (HMMs) have been proposed. In this paper we study their possibilities and potential benefits for the field of acoustic modeling. We describe preliminary experiments using an alternative modeling approach known as factorial hidden Markov models (FHMMs). We present these models as extensions of HMMs and detail a modification to the original formulation which seems to allow a more natural fit to speech. We present experimental results on the phonetically balanced TIMIT database comparing the performance of FHMMs with HMMs. We also study alternative feature representations that might be more suited to FHMMs