Jinyu Li, Ming Yuan, and Chin-Hui Lee
Inspired by the success of least absolute shrinkage and selection operator (LASSO) in statistical learning, we propose an regularized maximum likelihood linear regression (MLLR) to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing the standard MLLR objective function with an constraint. The so-called LASSO MLLR is a natural solution to the data insufficiency problem because the constraint regularizes some parameters to exactly 0 and reduces the number of free parameters to estimate. Tested on the 5k-WSJ0 task, the proposed LASSO MLLR gives significant word error rate reduction from the errors obtained with the standard MLLR in an utterance-by-utterance unsupervised adaptation scenario.
|Published in||ICML Workshop on Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing|