Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio

2025-01-20

AI Summary

This study identifies a critical hallucination vulnerability in the Whisper automatic speech recognition (ASR) model when exposed to non-speech audio—such as environmental sounds. Systematically injecting diverse non-speech adversarial signals, we characterize, for the first time, Whisper’s hallucination sensitivity patterns across noise types. To address this without model retraining, we propose the Bag-of-Hallucinations (BoH), a lightweight post-processing framework that statistically models and identifies high-frequency hallucinated tokens, enabling transcription purification. BoH integrates ASR robustness analysis, adversarial audio injection, and token-level hallucination modeling. Experiments across multiple real-world noise conditions demonstrate that BoH significantly reduces word error rate (WER), effectively suppresses hallucinated outputs, and enhances transcription reliability and safety. Our approach establishes a novel paradigm for trustworthy deployment of large-scale ASR models in open-domain, non-ideal acoustic environments.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Hallucinations of deep neural models are amongst key challenges in automatic speech recognition (ASR). In this paper, we investigate hallucinations of the Whisper ASR model induced by non-speech audio segments present during inference. By inducting hallucinations with various types of sounds, we show that there exists a set of hallucinations that appear frequently. We then study hallucinations caused by the augmentation of speech with such sounds. Finally, we describe the creation of a bag of hallucinations (BoH) that allows to remove the effect of hallucinations through the post-processing of text transcriptions. The results of our experiments show that such post-processing is capable of reducing word error rate (WER) and acts as a good safeguard against problematic hallucinations.

Problem

• Non-speech Sounds
• Whisper System
• Recognition Errors

Innovation

• Whisper Speech Recognition
• Non-Speech Sound Misidentification
• Hallucination Bundle Solution