Learning discriminative basis coefficients for eigenspace MLLR unsupervised adaptation

Abstract

Eigenspace MLLR is effective for fast adaptation when the amount of adaptation data is limited, e.g., less than 5s. The general motivation is to represent the MLLR transform as a linear combination of basis matrices. In this paper, we present a framework to estimate a speaker-independent discriminative transform over the combination coefficients. This… (More)
DOI: 10.1109/ICASSP.2013.6639208

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@article{Miao2013LearningDB, title={Learning discriminative basis coefficients for eigenspace MLLR unsupervised adaptation}, author={Yajie Miao and Florian Metze and Alexander H. Waibel}, journal={2013 IEEE International Conference on Acoustics, Speech and Signal Processing}, year={2013}, pages={7927-7931} }