Confidence-measure-driven unsupervised incremental adaptation for HMM-based speech recognition
Delphine Charlet · 2002
In this work, we first review the usual ways to take into account confidence measures in unsupervised adaptation and then propose a new unsupervised incremental adaptation based on a ranking of the adaptation data according to their confidence measures. A semi-supervised adaptation process is also proposed: the confidence measure is used to select the main part of the data for unsupervised adaptation and the remaining small part of the data is handled in a supervised mode. Experiments are conducted on a field database. Generic context-dependent phoneme HMMs are adapted to task- and field-specific conditions. These experiments show a significant improvement for unsupervised adaptation when confidence measures are used. In this work, we also show that the adaptation rate (that measures how important adaptation data are considered with respect to prior data) influences a lot the efficiency of the confidence measure in unsupervised adaptation.