1. A method comprising: receiving, at an external processing unit, speech from a user; processing the speech via an acoustic modeling process and a phoneme evaluation process; scoring Hidden Markov Models (HMMs) in association with the phoneme evaluation process by the following: receiving, at a co-processing unit, HMM information from an external processing unit, wherein the HMM information is derived from a plurality of HMMs, each HMM having a different type of data structure, wherein the different structures comprise Ergodic HMM structures, left-to-right structures, and parallel path left-to-right HMM structures; formatting, with the co-processing unit, the HMM information from each of the plurality of HMMs into a common HMM data structure to access the HMM information based on a priori knowledge of one or more fields and one or more indices in the common HMM data structure, wherein the formatting comprises formatting HMM information from at least one of a plurality of fields of the plurality of HMMs into the common HMM data structure; processing, with the co-processing unit, back pointer data and first HMM state scores for one or more NULL states in the common HMM data structure, each NULL state being a non-emitting state identified by a state-type flag; after processing the back pointer data and the first HMM state scores for each of the one or more NULL states in the common HMM data structure, processing, with the co-processing unit, second HMM state scores for one or more non-NULL states in the common HMM data structure based on at least one predecessor state; transferring the second HMM state scores from the co-processing unit to the external processing unit; and outputting, via the external processing unit, decoded speech based on the second HMM state scores.