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A Toolkit for Detecting Spurious Correlations in Speech Datasets

arXiv cs.AI / 4/30/2026

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Key Points

  • The paper introduces a publicly available research toolkit to detect spurious correlations between audio recording characteristics and target labels in speech datasets.
  • It argues that heterogeneous recording conditions—especially common in health-related speech data—can create artifacts that inflate reported model performance when present in both training and test sets.
  • The diagnostic method checks whether the target class can be inferred from non-speech regions of audio, where such inference suggests the presence of spurious (leakage) cues.
  • The authors position this as a safety-critical measure for high-stakes deployments, where overestimated performance could cause systems to fail minimum requirements.

Abstract

We introduce a toolkit for uncovering spurious correlations between recording characteristics and target class in speech datasets. Spurious correlations may arise due to heterogeneous recording conditions, a common scenario for health-related datasets. When present both in the training and test data, these correlations result in an overestimation of the system performance -- a dangerous situation, specially in high-stakes application where systems are required to satisfy minimum performance requirements. Our toolkit implements a diagnostic method based on the detection of the target class using only the non-speech regions in the audio. Better than chance performance at this task indicates that information about the target class can be extracted from the non-speech regions, flagging the presence of spurious correlations. The toolkit is publicly available for research use.

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