Title: How Distinguishable Are Vocoder Models? Analyzing Vocoder Fingerprints for Fake Audio. Abstract: In recent years, vocoders powered by deep neural networks (DNNs) have found much success in the task of generating raw waveforms from acoustic features, as the audio generated becomes increasingly realistic. This development however raises a few challenges, especially in the field of forensics, where the attribution of audio to real or generated sources is vital. To our knowledge, our investigation constitutes the first efforts to answer the question on the existence of vocoder fingerprints and to analyze them. In this paper, we present our discoveries in identifying the sources of generated audio waveforms. Our experiments conducted on the multi-speaker LibriTTS dataset show that (1) vocoder models do leave model-specific fingerprints on the audio they generate, and (2) minor differences in vocoder training can result in sufficiently different fingerprints in generated audio as to allow for distinguishing between the two. We believe that these differences are strong evidence that there exist vocoder-specific fingerprints that can be exploited for source identification purposes.