This work investigates the effects of reverberation and environmental noise on the generation of synthetic audio. The work focuses on how environmental factors can be used to detect deepfake/spoofed speech from bonafide speech. Advances in artificially generated speech have led to the technology being exploited for fraudulent activities and raised concerns over the integrity of evidence within audio and digital forensics. Much research has been undertaken into deepfake generation and detection in controlled environments; however, there has been little research into the effects of acoustic conditions on deepfake generation and detection. This work explores how environmental noise and reverberation influence the quality of deepfake generation and how these factors can be used to determine bonafide speech from spoofed speech. Speech was recorded in different acoustic environments and then used to train an adaptive TTS model to reproduce voice clones for each acoustic environment. The bonafide speech was then compared to the synthetic speech to analyse the quality of the spoofed speech. Comparisons between the spoofed speech and bonafide speech were analysed through reverberation time, spectral differences, and differences in average frequency distribution to explore the accuracy of the TTS model’s ability to reproduce speech and how environmental factors can influence detection of spoofed speech. Results showed that environmental noise and reverberation degraded the quality of spoofed speech, and the TTS model was unable to accurately reproduce reverb and elements of background noise.