  Speech classification has attracted increasing attention due to its wide
applications, particularly in classifying physical and mental states. However,
these tasks are challenging due to the high variability in speech signals.
Ensemble learning has shown promising results when multiple classifiers are
combined to improve performance. With recent advancements in hardware
development, combining several models is not a limitation in deep learning
research and applications. In this paper, we propose an uncertainty-based
ensemble learning approach for speech classification. Specifically, we train a
set of base features on the same classifier and quantify the uncertainty of
their predictions. The predictions are combined using variants of uncertainty
calculation to produce the final prediction. The visualization of the effect of
uncertainty and its ensemble learning results show potential improvements in
speech classification tasks. The proposed method outperforms single models and
conventional ensemble learning methods in terms of unweighted accuracy or
weighted accuracy.
