An end-to-end (e2e) text-to-speech (TTS) system is a deep architecture that learns to associate a text string with acoustic speech patterns from a dataset. It is expected that all aspects associated with speech production, such as phone duration, speaker characteristics, and intonation among other things are captured in the trained TTS model. Human speech is complex, involving smooth transitions between articulatory configurations (ACs). Due to anatomical constraints, some ACs are challenging to mimic or transition between. In this paper, we pose the question “Do e2e-TTS systems benefit by the constraints imposed by human anatomy?”. To answer this, we build systems using reverse text and reverse speech. Experiments demonstrate that e2e TTS systems are purely data-driven. Interestingly, the generated speech by reverse systems exhibits better fidelity, better perceptual intelligibility, and better naturalness.