Year: 2024
Type: conference-paper
Abstract: 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 com-bined 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 com-bined using variants of uncertainty calculation to produce the final prediction. The visualization of the effect of uncertainty and its ensemble learning results show potential improve-ments in speech classification tasks. The proposed method outperforms single models and conventional ensemble learning methods in terms of unweighted accuracy or weighted accuracy.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: 音声の音源-調音分解を用いた自己教師あり表現学習
OpenAlex ID: W4405633023