  This research explores how the information of prompts interacts with the
high-performing speech recognition model, Whisper. We compare its performances
when prompted by prompts with correct information and those corrupted with
incorrect information. Our results unexpectedly show that Whisper may not
understand the textual prompts in a human-expected way. Additionally, we find
that performance improvement is not guaranteed even with stronger adherence to
the topic information in textual prompts. It is also noted that English prompts
generally outperform Mandarin ones on datasets of both languages, likely due to
differences in training data distributions for these languages despite the
mismatch with pre-training scenarios. Conversely, we discover that Whisper
exhibits awareness of misleading information in language tokens by ignoring
incorrect language tokens and focusing on the correct ones. In sum, We raise
insightful questions about Whisper's prompt understanding and reveal its
counter-intuitive behaviors. We encourage further studies.
