  Recent strides in neural speech synthesis technologies, while enjoying
widespread applications, have nonetheless introduced a series of challenges,
spurring interest in the defence against the threat of misuse and abuse.
Notably, source attribution of synthesized speech has value in forensics and
intellectual property protection, but prior work in this area has certain
limitations in scope. To address the gaps, we present our findings concerning
the identification of the sources of synthesized speech in this paper. We
investigate the existence of speech synthesis model fingerprints in the
generated speech waveforms, with a focus on the acoustic model and the vocoder,
and study the influence of each component on the fingerprint in the overall
speech waveforms. Our research, conducted using the multi-speaker LibriTTS
dataset, demonstrates two key insights: (1) vocoders and acoustic models impart
distinct, model-specific fingerprints on the waveforms they generate, and (2)
vocoder fingerprints are the more dominant of the two, and may mask the
fingerprints from the acoustic model. These findings strongly suggest the
existence of model-specific fingerprints for both the acoustic model and the
vocoder, highlighting their potential utility in source identification
applications.
