Hidden Markov model speech recognition based on Kalman filtering

Traditional hidden Markov model speech recognition is generally based on a set of parameters (often LPC related) which are extracted at discrete intervals. Such an analysis necessitates use of a discrete-trial hidden Markov model in which the underlying states can only change at intervals related to the frame rate of the analysis. The exact locations of the analysis windows used can influence the front-end outputs and as a result can cause confusion between words differing in short-duration consonants. In the current study, an alternate method which does not require segmentation is proposed, and a simple version is implemented. The discrete trial hidden Markov model algorithms are adapted to this framework leading to significantly improved recognition performance.