Testing Speech Emotion Recognition Machine Learning Models
CoRR(2023)
摘要
Machine learning models for speech emotion recognition (SER) can be trained
for different tasks and are usually evaluated on the basis of a few available
datasets per task. Tasks could include arousal, valence, dominance, emotional
categories, or tone of voice. Those models are mainly evaluated in terms of
correlation or recall, and always show some errors in their predictions. The
errors manifest themselves in model behaviour, which can be very different
along different dimensions even if the same recall or correlation is achieved
by the model. This paper investigates behavior of speech emotion recognition
models with a testing framework which requires models to fulfill conditions in
terms of correctness, fairness, and robustness.
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