Currently the most popular acoustic model for speech recognition is the hidden Markov model (HMM). However, HMMs are based on a series of assumptions some of which are known to be poor. In particular, the assumption that successive speech frames are conditionally independent given the discrete state that generated them is not a good assumption for speech recognition. State space models may be used to address some shortcomings of this assumption. State space models are based on a continuous state vector evolving through time according to a state evo-

Automatic continuous speech recognition (CSR) is sufficiently

In this paper, we present a new approach to joint state and parameter estimation for a target-directed, nonlinear dynamic system model with switching states. The model is also called the hidden dynamic model (HDM) recently proposed for representing speech dynamics. The model parameters subject to statistical estimation consist of the target vector and the system matrix (also called the "time-constants"), as well as the parameters characterizing the non-linear mapping from the hidden state to the observation. These latter parameters are implemented in the current work as the weights of a three-layer feedforward multi-layer perceptron (MLP) network. The new estimation approach presented in this paper is based on the extended Kalman filter (EKF), and its performance is compared with the more traditional approach based on the expectation-maximisation (EM) algorithm. Extensive simulation experiment results are presented using the proposed EKF-based and the EM algorithms and under the typical conditions for employing the HDM for speech modeling. The results demonstrate superior convergence performance of the EKF-based algorithm compared with the EM algorithm, but the former su#ers from excessive computational loads when adopted for training the MLP weights.

This paper presents new results on evaluation of the statistical coarticulatory hidden dynamic model (HDM) on the TIMIT phone recognition task. We train both the HDM and baseline HMM on the complete TIMIT training data set and evaluate both systems using the N-best rescoring algorithm on the TIMIT test data set and the dr8 dialect subset. We show that with the inclusion of the reference transcription the HDM consistently outperforms the HMM for both 100-best+ref rescoring of the TIMIT test data and 1000-best+ref rescoring of the dr8 dialect subset with a reduction in the WER of between 3% and 6 % in all cases. We also verify the plausibility of the HDM paradigm by comparing plots of the model output with the observation data vectors.