Models observations of phoneme-based speech units as clusters of trajectories in their
parameter space. The trajectories are modeled by a mixture of state sequences of multi …

The basic motivation for employing trajectory models for speech recognition is that
sequences of speech features are statistically dependent and that the effective and efficient …

A new approach to represent temporal correlation in an automatic speech recognition
system is described. It introduces an acoustic feature set that captures the dynamics of a …

The key problem to be faced when building a HMM-based continuous speech recogniser is
maintaining the balance between model complexity and available training data. For large …

The authors propose a novel method of incorporating pitch information into a hidden Markov
model (HMM) phoneme recognizer by exploiting the correlation between pitch and spectral …

The authors describe an approach, the stochastic segment model, for context modeling in
continuous speech recognition for models based on multivariate Gaussian distributions …

This paper describes investigations into the use of linear dynamic segmental hidden Markov
models (SHMMs) for modelling speech feature-vector trajectories and their associated …

This paper proposes a new modeling of the structure of speech units as a graph consisting
of base functions and a transition network. A cluster algorithm taking into account the actual …

The author describes a segment-based HMM (hidden Markov model) recognizer and
presents phonetic recognition results achieved with the system. As opposed to a …

The authors present a segmental speech model that explicitly models the dynamics in a
variable-duration speech segment by using a time-varying trajectory model of the speech …