Speech sentiment systems are routinely benchmarked with gold transcripts, but a deployed cascade must read whatever its recognizer produces. We audit that gap on BanglaMUSE, a Bangla read-speech sentiment corpus, using sentence-grouped cross-validation and a controlled substitution design in which one trained classifier is evaluated three times while only the test-time transcript changes. Replacing gold transcripts with ASR output costs 0.109 macro-F1 with Bengali XLS-R CTC and 0.341 with our Whisper large-v3 configuration, a high-error stress case. Retraining the classifier on matched recognizer output recovers 57–81% of the gap, but re-selecting only the decision threshold on ASR validation data, with weights and probabilities unchanged, already recovers 28–49%. Adding the speech embedding improves over ASR text, yet replacing it with acquisition/nuisance features gives a similar gain for gold-trained heads. These features alone predict sentiment at 0.669 macro-F1 (group-permutation null mean 0.495, p = 0.001), and a speech-only classifier that reaches 0.940 falls to 0.621 when both speaker and sentence are unseen. These speech-only and fusion results therefore do not provide clean evidence of prosodic sentiment cues. Low-resource speech–text benchmarks should report oracle and deployable conditions, separate threshold effects from retraining, and test acquisition variables before interpreting audio.