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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...
Recently, we proposed an ensemble speaker and speaking environment modeling (ESSEM) framework to characterize speaker variability and speaking environments. In contrast to multi-style training, ESSEM uses single-style training to prepare multiple sets of environment-specific acoustic models. The ensemble of these acoustic models forms a prior structure of the environment for flexible prediction of...
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