Maximum likelihood non-linear transformation for environment adaptation in speech recognition
Mukund Padmanabhan, Satya Dharanipragada · 2001
Abstract In this paper, we describe an adaptation method for speechrecognition systems that is based on a piecewise-linear approx-imation to a non-linear transformation of the feature space.The method extends a previously proposed non-linear trans-formation (NLT) technique by making the transformation func-tion more sophisticated (piecewise-linear instead of piecewise-constant), and by computing the transformation to maximizethe likelihood of the adaptation data given its transcription (in-stead of just matching the global statistics of the test and train-ing data). This method also differs from other linear techniques(such as MLLR, linear feature space transforms, etc.) in twoways - first, the computed transformation is non-linear, second,the tying structure of the transformation depends not on the pho-netic class but rather on the location in the feature space. Exper-imental results show that the method performs well for the caseof limited adaptation data, and the performance gains appear tobe additive to those provided by MLLR - yielding upto 3.4%relative improvement over MLLR.