Aspect-model-based reference speaker weighting

We propose an aspect-model-based reference speaker weighting. The main idea of the approach is that the adapted model is a linear combination of a set of reference speakers like reference speaker weighting (RSW) and eigenvoices. The aspect model is the mixture model of speaker-dependent (SD) models. In this paper, aspect model weighting (AMW) is proposed for finding an optimal weighting of a set of reference speakers unlike RSW and the aspect model which is a kind of cluster models is trained based on likelihood maximization with respect to the training data. The number of adaptation parameters can also be reduced using aspect model approach. For evaluation, we carried out an isolated word recognition experiment on Korean database (KLE452). The results were compared to those of conventional MAP, MLLR, RSW, and eigenvoice. Even though we use only 0.5s of adaptation data, 27.24% relative error rate reduction in comparison with speaker-independent (SI) baseline performance was achieved.