An EKF-Based Algorithm for Learning Statistical Hidden Dynamic Model Parameters for Phonetic Recognition

This paper presents a new parameter estimation algorithm based on the Extended Kalman Filter (EKF) for the recently proposed statistical coarticulatory Hidden Dynamic Model (HDM). We show how the EKF parameter estimation algorithm unifies and simplifies the estimation of both the state and parameter vectors. Experiments based on N-best rescoring demonstrate superior performance of the (context independent) HDM over a triphone baseline HMM in the TIMIT phonetic recognition task. We also show that the HDM is capable of generating speech vectors close to those from the corresponding real data.