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

Yuan Gong; Sameer Khurana; Leonid Karlinsky; James Glass

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Paper · arXiv 2307.03183

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

Yuan Gong, Sameer Khurana, Leonid Karlinsky, James Glass

10 upvotesJuly 6, 2023arXiv 预印本

AI 摘要

Whisper-AT, a unified model built upon Whisper, extends speech recognition to audio event tagging with minimal extra computational cost.

automatic speech recognitionWhisperlabeled speech corpusbackground soundsaudio representationnoise-invariantnon-speech soundsaudio taggingbackboneforward pass

Abstract

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.