How Much Prosody Does Your ASR Encoder Already Know

End-of-turn (EoT) detection decides in real-time whether a speaker has finished. Traditional detectors rely on hand-built prosodic features (pitch, speaking rate, pauses), while modern detectors run on a neural ASR encoder that has processed the same audio. Because the encoder reads the signal those features are computed from, it may already hold much of the information they were meant to supply. We test this three ways, reading a streaming Zipformer encoder at four depths from raw input to the final layer. First, we measure how much prosody helps over the encoder alone. Second, we test whether prosody can be recovered from the encoder. Third, we ask whether any single feature still helps given the encoder. The three tests agree; prosody adds a large gain on raw audio, but that gain collapses once the signal passes through even the first encoder layer. The same pattern holds for two open-source encoders, wav2vec2 and Whisper, both of which are offline.