Segment-based stochastic models of spectral dynamics for continuous speech recognition
Vassilios Digalakis · 1992
This dissertation addresses the problem of modeling the joint time-spectral structure of speech for recognition. Four areas are covered in this work: segment modeling, estimation, recognition search algorithms, and extension to a more general class of models. A unified view of the acoustic models that are currently used in speech recognition is presented; the research is then focused on segment-based models that provide a better framework for modeling the intrasegmental statistical dependencies than the conventional hidden Markov models (HMMs). The validity of a linearity assumption for modeling the intrasegmental statistical dependencies is first checked, and it is shown that the basic assumption of conditionally independent observations given the underlying state sequence that is inherent to HMMs is inaccurate. Based on these results, linear models are chosen for the distribution of the observations within a segment of speech. Motivated by the original work of the stochastic segment model, a dynamical system segment model is proposed for continuous speech recognition. Training of this model is equivalent to the maximum likelihood identification of a stochastic linear system, and a simple alternative to the traditional approach is developed. This procedure is based on the ExpectationMaximization algorithm and is analogous to the Baum-Welch algorithm for HMMs, since the dynamical system segment model can be thought of as a continuous state vii HMM. Recognition involves computing the probability of the innovations given by Kalman filtering. The large computational complexity of segment-based models is dealt with by the introduction of fast recognition search algorithms as alternatives to the typical Dynamic Programming search. A Split-and-Merge segmentation algorithm is...