Adaptation of polynomial trajectory segment models for large vocabulary speech recognition

Segment models are a generalization of HMMs that can represent feature dynamics and/or correlation in time. We develop the theory of Bayesian and maximum-likelihood adaptation for a segment model characterized by a polynomial mean trajectory. We show how adaptation parameters can be shared and adaptation detail can be controlled at run-time based on the amount of adaptation data available. Results on the Switchboard corpus show error reductions for unsupervised transcription mode adaptation and supervised batch mode adaptation