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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 (contextindependent) HDM over a triphone baseline HMM in the TIMIT phonetic recognition task. We also show thatdoi:10.1109/icassp.2001.940868 dblp:conf/icassp/TogneriD01 fatcat:64yv3e3uvvgund32lgaaqadlyu