  By implicitly recognizing a user based on his/her speech input, speaker
identification enables many downstream applications, such as personalized
system behavior and expedited shopping checkouts. Based on whether the speech
content is constrained or not, both text-dependent (TD) and text-independent
(TI) speaker recognition models may be used. We wish to combine the advantages
of both types of models through an ensemble system to make more reliable
predictions. However, any such combined approach has to be robust to incomplete
inputs, i.e., when either TD or TI input is missing. As a solution we propose a
fusion of embeddings network foenet architecture, combining joint learning with
neural attention. We compare foenet with four competitive baseline methods on a
dataset of voice assistant inputs, and show that it achieves higher accuracy
than the baseline and score fusion methods, especially in the presence of
incomplete inputs.
