HMM adaptation using sparse Probabilistic Space Mapping for noisy speech

This paper presents an extension of Probabilistic Space Maps (PS-MAPS) to adapt clean acoustic models to a noisy environment. In the presence of noise, the relationship between noisy and clean speech features (MFCC´s, HDLA, etc.) is either nonlinear or unknown. Given the relationship between features, traditional methods try to linearize it using approximations. These methods cannot be used for systems where the mapping model for clean and noisy features is missing. Given sufficient training data, PS-MAPS provides an excellent framework for extracting and modeling this relationship. The PS-MAP based approach to model adaptation is completely data driven. Experiments were performed on Aurora 2 dataset to evaluate the effectiveness of the algorithm.