The Anatomy of an ASR Hallucination
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Abstract (arXiv)
ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucinations as one possible consequence of a broader grounding failure, in which the transcript is no longer adequately guided by the audio. To understand where this failure becomes possible, we study two independently trained Conformer-Large recognizers - one CTC and one RNN-T - under environmental degradation and speaker-background shift. In both models, the final encoder stage emerges as a critical boundary: bypassing the final block causes divergence on nearly every utterance, whereas bypassing middle blocks has little effect. At this same stage, the representations become more compact, text becomes readable by the trained decoder, and grapheme information becomes explicit. Importantly, the intervention produces garbled or repetitive output rather than fluent fabrication. Our result therefore identifies a mechanistic precondition for hallucination - the failure to produce adequately grounded output - not the complete origin of naturally occurring hallucinations. Together, the results reveal a consistent terminal-stage dependency for grounded recognition across two decoder families and multiple distribution shifts.
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References · 16
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
- 2019Similarity of Neural Network Representations Revisitedcited by 1,695
- 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
- 2024INSIDE: LLMs' Internal States Retain the Power of Hallucination Detectioncited by 317
- 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 293
- 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 246
- 2019NeMo: a toolkit for building AI applications using Neural Modulescited by 230
- 2020Hybrid Autoregressive Transducer (hat)cited by 106
- 2020Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognitioncited by 80
- 2021Scikit-dimension: a Python package for intrinsic dimension estimationcited by 68
- 2025Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Modelscited by 17
4 more
- 2025Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Downcited by 12
- 2025Beyond Transcription: Mechanistic Interpretability in ASRcited by 12
- 2022Adaptive Discounting of Implicit Language Models in RNN-Transducerscited by 5
- 2023Improving RNN-Transducers with Acoustic LookAheadcited by 4
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