  In this paper, we focus on Whisper, a recent automatic speech recognition
model trained with a massive 680k hour labeled speech corpus recorded in
diverse conditions. We first show an interesting finding that while Whisper is
very robust against real-world background sounds (e.g., music), its audio
representation is actually not noise-invariant, but is instead highly
correlated to non-speech sounds, indicating that Whisper recognizes speech
conditioned on the noise type. With this finding, we build a unified audio
tagging and speech recognition model Whisper-AT by freezing the backbone of
Whisper, and training a lightweight audio tagging model on top of it. With <1%
extra computational cost, Whisper-AT can recognize audio events, in addition to
spoken text, in a single forward pass.
