Implicit trajectory modeling through Gaussian Transition Models for speech recognition

It is well known that frame independence assumption is a fundamental limitation of current HMM based speech recognition systems. By treating each speech frame independently, HMMs fail to capture trajectory information in the acoustic signal. This paper introduces Gaussian Transition Models (GTM) to model trajectories implicitly. Comparing to alternative approaches, such as segment modeling and parallel path HMM, GTM has the advantage that it integrates seamlessly with the HMM frame-work; it can model a large number of trajectories and there is no need to define a topology a priori. Preliminary experiments on Switchboard, a large vocabulary conversational speech recognition task, have shown promising results.