“Recent advancements in neural speech synthesis technologies have brought aboutwidespread applications but have also raised concerns about potential misuse and abuse.Addressing these challenges is crucial, particularly in the realms of forensics and intellec-tual property protection. While previous research on source attribution of synthesizedspeech has its limitations, our study aims to fill these gaps by investigating the identifi-cation of sources in synthesized speech. We focus on analyzing speech synthesis modelfingerprints in generated speech waveforms, emphasizing the roles of the acoustic modeland vocoder. Our research, based on the multi-speaker LibriTTS dataset, reveals twokey insights: (1) both vocoders and acoustic models leave distinct, model-specific fin-gerprints on generated waveforms, and (2) vocoder fingerprints, being more dominant,may obscure those from the acoustic model. These findings underscore the presence ofmodel-specific fingerprints in both components, suggesting their potential significance insource identification applications.”