  Generative models are now capable of synthesizing images, speeches, and
videos that are hardly distinguishable from authentic contents. Such
capabilities cause concerns such as malicious impersonation and IP theft. This
paper investigates a solution for model attribution, i.e., the classification
of synthetic contents by their source models via watermarks embedded in the
contents. Building on past success of model attribution in the image domain, we
discuss algorithmic improvements for generating user-end speech models that
empirically achieve high attribution accuracy, while maintaining high
generation quality. We show the trade off between attributability and
generation quality under a variety of attacks on generated speech signals
attempting to remove the watermarks, and the feasibility of learning robust
watermarks against these attacks.
