Acoustic Modeling Based on Deep Conditional Random Fields

Acoustic modeling based on Hidden Markov Models (HMMs) is employed by state-of-theart stochastic speech recognition systems. In continuous density HMMs, the state scores are computed using Gaussian mixture models. On the other hand, Deep Neural Networks (DNN) can be used to compute the HMM state scores. This leads to significant improvement in the recognition accuracy. Conditional Random Fields (CRFs) are undirected graphical models that maintain the Markov properties of Hidden Markov Models (HMMs), formulated using the maximum entropy (MaxEnt) principle. It is possible to use DNN to compute the state scores in CRFs. Using CRFs on the top of DNN will lead to an acoustic model known as Deep Conditional Random Fields (DCRFs). In this paper, we present a phone recognition task based on DCRFs. Preliminary results on the TIMIT task show that DCRFs can lead to good results.