  Advances in neural speech synthesis have brought us technology that is not
only close to human naturalness, but is also capable of instant voice cloning
with little data, and is highly accessible with pre-trained models available.
Naturally, the potential flood of generated content raises the need for
synthetic speech detection and watermarking. Recently, considerable research
effort in synthetic speech detection has been related to the Automatic Speaker
Verification and Spoofing Countermeasure Challenge (ASVspoof), which focuses on
passive countermeasures. This paper takes a complementary view to generated
speech detection: a synthesis system should make an active effort to watermark
the generated speech in a way that aids detection by another machine, but
remains transparent to a human listener. We propose a collaborative training
scheme for synthetic speech watermarking and show that a HiFi-GAN neural
vocoder collaborating with the ASVspoof 2021 baseline countermeasure models
consistently improves detection performance over conventional classifier
training. Furthermore, we demonstrate how collaborative training can be paired
with augmentation strategies for added robustness against noise and
time-stretching. Finally, listening tests demonstrate that collaborative
training has little adverse effect on perceptual quality of vocoded speech.
