  Speech emotion recognition (SER) has gained significant attention due to its
several application fields, such as mental health, education, and
human-computer interaction. However, the accuracy of SER systems is hindered by
high-dimensional feature sets that may contain irrelevant and redundant
information. To overcome this challenge, this study proposes an iterative
feature boosting approach for SER that emphasizes feature relevance and
explainability to enhance machine learning model performance. Our approach
involves meticulous feature selection and analysis to build efficient SER
systems. In addressing our main problem through model explainability, we employ
a feature evaluation loop with Shapley values to iteratively refine feature
sets. This process strikes a balance between model performance and
transparency, which enables a comprehensive understanding of the model's
predictions. The proposed approach offers several advantages, including the
identification and removal of irrelevant and redundant features, leading to a
more effective model. Additionally, it promotes explainability, facilitating
comprehension of the model's predictions and the identification of crucial
features for emotion determination. The effectiveness of the proposed method is
validated on the SER benchmarks of the Toronto emotional speech set (TESS),
Berlin Database of Emotional Speech (EMO-DB), Ryerson Audio-Visual Database of
Emotional Speech and Song (RAVDESS), and Surrey Audio-Visual Expressed Emotion
(SAVEE) datasets, outperforming state-of-the-art methods. To the best of our
knowledge, this is the first work to incorporate model explainability into an
SER framework. The source code of this paper is publicly available via this
https://github.com/alaaNfissi/Unveiling-Hidden-Factors-Explainable-AI-for-Feature-Boosting-in-Speech-Emotion-Recognition.
