Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers

James Glass; Leonid Karlinsky; Sameer Khurana; Yuan Gong

Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers

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arxiv 2307.03183 v1 pith:KPBG7WMO submitted 2023-07-06 cs.SD eess.AS

Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers

Yuan Gong , Sameer Khurana , Leonid Karlinsky , James Glass This is my paper

classification cs.SD eess.AS

keywords audiospeechwhispermodelwhisper-atautomaticfindingrecognition

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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.

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