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Found 2 papers, 1 papers with code
Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition
We compare biases found in pre-trained models to biases in downstream models adapted to the task of Speech Emotion Recognition (SER) and find that in 66 of the 96 tests performed (69%), the group that is more associated with positive valence as indicated by the SpEAT also tends to be predicted as speaking with higher valence by the downstream model.
The Role of Individual User Differences in Interpretable and Explainable Machine Learning Systems
There is increased interest in assisting non-expert audiences to effectively interact with machine learning (ML) tools and understand the complex output such systems produce.
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