A Toolkit for Detecting Spurious Correlations in Speech Datasets

Problem
This paper addresses the gap in the detection of spurious correlations in speech datasets, particularly in health-related applications where recording conditions can vary significantly. The authors highlight that such spurious correlations can lead to an overestimation of model performance, which poses risks in high-stakes scenarios where systems must meet stringent performance criteria. The work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce a diagnostic toolkit designed to identify spurious correlations by analyzing non-speech regions of audio recordings. The core technical contribution is a method that assesses the ability to predict the target class using only the non-speech segments of the audio. If the model achieves better-than-chance performance in this task, it indicates that the non-speech regions contain information correlated with the target class, thus flagging potential spurious correlations. The toolkit is implemented in Python and is publicly available for research purposes, facilitating reproducibility and further exploration of the issue.

Results
The authors demonstrate the effectiveness of their toolkit through empirical evaluations on various speech datasets. They report that their method successfully identifies spurious correlations, with specific performance metrics indicating that models trained on datasets with such correlations can achieve accuracy levels significantly above random chance. While exact numerical results and comparisons to baseline models are not provided in the abstract, the implication is that the toolkit can reliably detect problematic correlations that would otherwise inflate performance metrics.

Limitations
The authors acknowledge that their toolkit primarily focuses on the detection of spurious correlations and does not provide solutions for mitigating these issues once identified. Additionally, the effectiveness of the toolkit may vary depending on the specific characteristics of the datasets used, such as the diversity of recording conditions and the nature of the target classes. The authors do not discuss potential biases in the datasets themselves or the generalizability of their findings across different domains beyond health-related applications.

Why it matters
This work has significant implications for the development and evaluation of machine learning models in speech processing, particularly in sensitive applications like healthcare. By providing a systematic approach to uncovering spurious correlations, the toolkit enables researchers and practitioners to critically assess the validity of their models’ performance metrics. This is crucial for ensuring that deployed systems are robust and reliable, ultimately leading to safer and more effective applications in real-world scenarios. The availability of the toolkit encourages further research into the prevalence of spurious correlations in various datasets, fostering a more rigorous understanding of model performance in the field.

Authors: Lara Gauder, Pablo Riera, Andrea Slachevsky, Gonzalo Forno, Adolfo M. García, Luciana Ferrer
Source: arXiv:2604.26676
URL: https://arxiv.org/abs/2604.26676v1