“Whisper-AT: Noise-Robust Automatic Speech Recognizers Are Also Strong General Audio Event Taggers”, Yuan Gong, Sameer Khurana, Leonid Karlinsky, James Glass2023-07-06 (AI music, Whisper NN)⁠:

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 (eg. 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.