Rapid speaker adaptation using compressive sensing
A new speaker adaptation framework using compressive sensing is proposed. A speaker dictionary is constructed using all eigenvoices and training speaker models. Optimal sparse representation of a speaker model is constructed using the dictionary. Matching pursuit and L1 regularized optimization are adapted to solve the problem. The new methods outperform the conventional ones under all testing conditions.
speaker adaptation compressive sensing matching pursuit speaker subspace l1 l 1 regularization l1 l 1 l1 l 1 l 1