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Investigating online low-footprint speaker adaptation using generalized linear regression and click-through data
2015
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
To develop speaker adaptation algorithms for deep neural network (DNN) that are suitable for large-scale online deployment, it is desirable that the adaptation model be represented in a compact form and learned in an unsupervised fashion. In this paper, we propose a novel low-footprint adaptation technique for DNN that adapts the DNN model through node activation functions. The approach introduces slope and bias parameters in the sigmoid activation functions for each speaker, allowing the
doi:10.1109/icassp.2015.7178784
dblp:conf/icassp/ZhaoLXG15
fatcat:btba63cxlfbyjbqwob2w5g2aye