In this paper, we extend the maximum likelihood
(ML) training algorithm to the minimum classification error
(MCE) training algorithm for discriminatively estimating the
state-dependent polynomial coefficients in the stochastic trajectory
model or the trended hidden Markov model (HMM)
originally proposed in [2]. The main motivation of this extension
is the new model space for smoothness-constrained, state-bound
speech trajectories associated with the trended HMM, contrasting
the conventional, stationary-state HMM, which describes only
the piecewise-constant “degraded trajectories” in the observation
data. The discriminative training implemented for the trended
HMM has the potential to utilize this new, constrained model
space, thereby providing stronger power to disambiguate the
observational trajectories generated from nonstationary sources
corresponding to different speech classes. Phonetic classification
results are reported which demonstrate consistent performance
improvements with use of the MCE-trained trended HMM both
over the regular ML-trained trended HMM and over the MCEtrained
stationary-state HMM.