  The advancements of AI-synthesized human voices have introduced a growing
threat of impersonation and disinformation. It is therefore of practical
importance to developdetection methods for synthetic human voices. This work
proposes a new approach to detect synthetic human voices based on identifying
artifacts of neural vocoders in audio signals. A neural vocoder is a specially
designed neural network that synthesizes waveforms from temporal-frequency
representations, e.g., mel-spectrograms. The neural vocoder is a core component
in most DeepFake audio synthesis models. Hence the identification of neural
vocoder processing implies that an audio sample may have been synthesized. To
take advantage of the vocoder artifacts for synthetic human voice detection, we
introduce a multi-task learning framework for a binary-class RawNet2 model that
shares the front-end feature extractor with a vocoder identification module. We
treat the vocoder identification as a pretext task to constrain the front-end
feature extractor to focus on vocoder artifacts and provide discriminative
features for the final binary classifier. Our experiments show that the
improved RawNet2 model based on vocoder identification achieves an overall high
classification performance on the binary task.
