  Advancements in AI-synthesized human voices have created a growing threat of
impersonation and disinformation, making it crucial to develop methods to
detect synthetic human voices. This study proposes a new approach to
identifying synthetic human voices by detecting artifacts of vocoders in audio
signals. Most DeepFake audio synthesis models use a neural vocoder, a neural
network that generates waveforms from temporal-frequency representations like
mel-spectrograms. By identifying neural vocoder processing in audio, we can
determine if a sample is synthesized. To detect synthetic human voices, we
introduce a multi-task learning framework for a binary-class RawNet2 model that
shares the feature extractor with a vocoder identification module. By treating
vocoder identification as a pretext task, we constrain the 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 high classification performance on the
binary task overall.
