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Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models
Atwany, Hanin Waheed, Abdul Singh, Rita Choudhury, Monojit Raj, Bhiksha
Atwany, Hanin
Waheed, Abdul
Singh, Rita
Choudhury, Monojit
Raj, Bhiksha
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Natural Language Processing
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Conference proceeding
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http://creativecommons.org/licenses/by/4.0/
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Abstract
Speech foundation models trained at a massive scale, both in terms of model and data size, result in robust systems capable of performing multiple speech tasks, including automatic speech recognition (ASR). These models transcend language and domain barriers, yet effectively measuring their performance remains a challenge. Traditional metrics like word error rate (WER) and character error rate (CER) are commonly used to evaluate ASR performance but often fail to reflect transcription quality in critical contexts, particularly when detecting fabricated outputs. This phenomenon, known as hallucination, is especially concerning in high-stakes domains such as healthcare, legal, and aviation, where errors can have severe consequences. In our work, we address this gap by investigating hallucination in ASR models. We examine how factors such as distribution shifts, model size, and model architecture influence the hallucination error rate (HER), a metric we introduce to quantify hallucinations. Our analysis of over 20 ASR models reveals key insights: (1) High WERs can mask low hallucination rates, while low WERs may conceal dangerous hallucinations. (2) Synthetic noise, both adversarial and common perturbations like white noise, pitch shift, and time stretching, increase HER. (3) Distribution shift correlates strongly with HER (𝛼 = 0.91). Our findings highlight the importance of incorporating HER alongside traditional metrics like WER to better assess ASR model performance, particularly in high-stakes domains.
Citation
H. Atwany, A. Waheed, R. Singh, M. Choudhury, B. Raj, "Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models," 2025, pp. 23181-23203.
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Proceedings of the Annual Meeting of the Association for Computational Linguistics
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Findings of the Association for Computational Linguistics: ACL 2025
Keywords
47 Language, Communication and Culture, 4704 Linguistics
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Findings of the Association for Computational Linguistics: ACL 2025
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Association for Computational Linguistics