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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

Yuan Gong , Sameer Khurana , Leonid Karlinsky , James Glass This is my paper · ORCID
classification cs.SDeess.AS
keywords audiospeechwhispermodelwhisper-atautomaticfindingrecognition
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fusion Embedding: A Unified Embedding Space for Text, Image, Video, and Audio

    cs.CL 2026-07 conditional novelty 7.0 of 10

    Trained connectors and audio-only gated adapters integrate audio into a frozen vision-language embedding space, preserving base outputs bit-exactly and yielding emergent audio-image retrieval.

  2. SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models

    cs.CL 2026-07 conditional novelty 6.0 of 10

    An open-source benchmark for speech-to-speech models shows that current systems produce intelligible audio but diverge from human conversational behavior in latency, dialect consistency, emotional entrainment, and prosody.

  3. LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model

    cs.CL 2025-08 unverdicted novelty 6.0 of 10

    LLaSO releases a 3.8B speech-language model, 25.5M training instances, and an evaluation benchmark, claiming a normalized score of 0.72.

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