# All I Hear is Noise: Investigating Clever Hans Effects in Clinical Speech Datasets

arXiv: https://arxiv.org/abs/2609.21080 · HTML: https://mlsanity.com/paper/2609.21080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2026.

## Built on by (most cited first, 1 of 1)

- [Beyond Decodability: Do Acoustic Factors Drive Predictions in Speech-Based Alzheimer's Assessment?](https://mlsanity.com/paper/2610.01846/) (2026)

## Abstract (arXiv)

Recent work revealed a striking Clever Hans effect in the Pitt dataset, where Alzheimer's detection achieved nearly 100% accuracy using only silent audio segments. This raises serious concerns about hidden confounding factors in speech-based health datasets. We systematically investigate whether similar biases exist across five widely used clinical speech corpora: DAIC-WoZ (depression), TORGO (dysarthria), Neurovoz (PD), MDVR-KCL (PD), and UCLASS (stuttering). For each dataset, we compare classification using the first second of audio, silent segments, and full recordings, and evaluate both raw and denoised signals. Across all datasets, silence-only classification frequently matched or exceeded full-audio performance, suggesting that classification performance may be influenced by dataset-specific confounds in addition to disorder-related speech characteristics. These findings question the reliability and generalisability of speech-based biomarkers and call for stricter methodological reporting, preprocessing transparency, and bias mitigation.