- Seongjun Hahm, Shinji Watanabe, M. Fujimoto, A. Ogawa, Takaaki Hori, Atsushi Nakamura. 2012. NORMALIZATION AND ADAPTATION BY CONSISTENTLY EMPLOYING MAP ESTIMATION. Abstract: The purpose of this paper is to develop a feature and model space adaptation method that improves speech recognition performance regardless of the amount of adaptation data. We propose a maximum a posteriori (MAP)-based Bayesian estimation approach that is consistently applied to normalization of feature spaces and adaptation of model parameters(i.e., maximum a posteriori linear regression (MAPLR) approach for both spaces). Namely, we use feature space MAPLR (fMAPLR) for the feature space normalization and structural MAPLR (SMAPLR) for the model parameter adaptation. The proposed approach utilizes the both advantages of fMAPLR in the small amount of data and SMAPLR in the large amount of data. Furthermore, bysharing the same prior distribution between fMAPLR and SMAPLR, we keep the consistency of the adaptation in different two spaces and appropriately regularizing the parameter estimation in both spaces. Experiments on large vocabulary continuous speech recognition using a Corpus of Spontaneous Japanese (CSJ) task confirm that the proposed approach outperformed other conventional adaptation approaches especially when the adaptation data is small.
