Recent advances in deep-learning have pushed text-to-speech synthesis (TTS) very close to human speech. In deep-learning, latent features refer to features that are hidden from us; notwithstanding, we may meaningfully observe their effects. Analogously, latent prosodic features refer to the exact features that constitute e.g. prominence that are unknown to us, although we know (some of) the functions of prominence and (some of) its acoustic correlates. Deep-learned speech models capture prosody well, but leave us with little control and few insights. Previously, we explored average syllable duration on word level - a simple and accessible metric - as a proxy for prominence: in Swedish TTS, where verb particles and numerals tend to receive too little prominence, these were nudged towards lengthening while allowing the TTS models to otherwise operate freely. Listener panels overwhelmingly preferred the nudged versions to the unmodified TTS. In this paper, we analyse utterances from the modified TTS. The analysis shows that duration-nudging of relevant words changes the following features in an observable manner: duration is predictably lengthened, word-initial glottalization occurs, and the general intonation pattern changes. This supports the view of latent prosodic features that can be reflected in deep-learned models and accessed by proxy.