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