Investigating online low-footprint speaker adaptation using generalized linear regression and click-through data

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 adaptation model to be stored in a small-sized storage space. We show that this adaptation technique can be formulated in a linear regression fashion, analogous to other speak adaptation algorithms that apply additional linear transformations to the DNN layers. We further investigate semi-supervised online adaptation by making use of the user click-through data as a supervision signal. The proposed method is evaluated on short message dictation and voice search tasks in supervised, unsupervised, and semi-supervised setups. Compared with the singular value decomposition (SVD) bottleneck adaptation, the proposed adaptation method achieves comparable accuracy improvements with much smaller footprint.