Unsupervised adaptation using structural Bayes approach
Koichi Shinoda, Chin‐Hui Lee · 2002
It is well-known that the performance of recognition systems is often largely degraded when there is a mismatch between the training and testing environment. It is desirable to compensate for the mismatch when the system is in operation without any supervised learning. Previously, a structural maximum a posteriori (SMAP) adaptation approach, in which a hierarchical structure in the parameter space is assumed, was proposed. In this paper, this SMAP method is applied to unsupervised adaptation. A novel normalization technique is also introduced as a front end for the adaptation process. The recognition results showed that the proposed method was effective even when only one utterance from a new speaker was used for adaptation. Furthermore, an effective way to combine the supervised adaptation and the unsupervised adaptation was investigated to reduce the need for a large amount of supervised learning data.