@inproceedings{li2010shrinkage,
author = {Li, Jinyu and Tsao, Yu and Lee, Chin-Hui},
title = {Shrinkage Model Adaptation in Automatic Speech Recognition},
booktitle = {Interspeech},
year = {2010},
month = {January},
abstract = {We propose a parameter shrinkage adaptation framework to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing an objective function with a sum of parameter-wise power q constraint. For the first attempt, we formulate ridge maximum likelihood linear regression (MLLR) and ridge constraint MLLR (CMLLR) with an element-wise square sum constraint to regularize the objective functions of the conventional MLLR and CMLLR, respectively. Tested on the 5k-WSJ0 task, the proposed ridge MLLR and ridge CMLLR algorithms give significant word error rate reduction from the errors obtained with standard MLLR and CMLLR in an utterance-by-utterance unsupervised adaptation scenario.},
url = {http://approjects.co.za/?big=en-us/research/publication/shrinkage-model-adaptation-in-automatic-speech-recognition/},
edition = {Interspeech},
}