Model Adaptation for Automatic Speech Recognition Based on Multiple Time Scale Evolution

The change in speech characteristics is originated from various factors, at various (temporal) rates in a real world conversation. These temporal changes have their own dynamics and therefore, we propose to extend the single (time-) incremental adaptations to a multiscale adaptation, which has the potential of greatly increasing the model's robustness as it will include adaptation mechanism to approximate the nature of the characteristic change. The formulation of the incremental adaptation assumes a time evolution system of the model, where the posterior distributions, used in the decision process, are successively updated based on a macroscopic time scale in accordance with the Kalman filter theory. In this paper, we extend the original incremental adaptation scheme, based on a single time scale, to multiple time scales, and apply the method to the adaptation of both the acoustic model and the language model. We further investigate methods to integrate the multi-scale adaptation scheme to realize the robust speech recognition performance. Large vocabulary continuous speech recognition experiments for English and Japanese lectures revealed the importance of modeling multiscale properties in speech recognition.