Recurrent input transformations for hidden Markov models

A novel architecture which integrates recurrent input transformation (RITs) and continuous density hidden Markov models (HMMs) is presented. The basic HMM structure is extended to accommodate recurrent neural networks which transform the input observations before they enter the Gaussian output distributions associated with the states of the HMM. During training the parameters of both the HMM and the RIT are simultaneously optimized according to the maximum mutual information (MMI) criterion. Results for the E-set recognition task are presented, demonstrating the ability of RITs to exploit longer-term correlations in the speech signal and to give improved discrimination.<>