In speech emotion recognition (SER), using predefined features without
considering their practical importance may lead to high dimensional datasets,
including redundant and irrelevant information. Consequently, high-dimensional
learning often results in decreasing model accuracy while increasing
computational complexity. Our work underlines the importance of carefully
considering and analyzing features in order to build efficient SER systems. We
present a new supervised SER method based on an efficient feature engineering
approach. We pay particular attention to the explainability of results to
evaluate feature relevance and refine feature sets. This is performed
iteratively through feature evaluation loop, using Shapley values to boost
feature selection and improve overall framework performance. Our approach
allows thus to balance the benefits between model performance and transparency.
The proposed method outperforms human-level performance (HLP) and
state-of-the-art machine learning methods in emotion recognition on the TESS
dataset.

本论文提出了一种基于高效特征工程方法的监督情感识别方法，通过特征评估循环中的 Shapley 值来提高特征选择和整体框架性能，以平衡模型性能和透明度。该方法在 TESS 数据集上在情感识别方面优于人的水平和现有的机器学习方法。

迭代特征增强用于可解释的语音情感识别

Iterative Feature Boosting for Explainable Speech Emotion Recognition

We present novel, computationally efficient, and differentially private
algorithms for two fundamental high-dimensional learning problems: learning a
multivariate Gaussian and learning a product distribution over the Boolean
hypercube in total variation distance. The sample complexity of our algorithms
nearly matches the sample complexity of the optimal non-private learners for
these tasks in a wide range of parameters, showing that privacy comes
essentially for free for these problems. In particular, in contrast to previous
approaches, our algorithm for learning Gaussians does not require strong a
priori bounds on the range of the parameters. Our algorithms introduce a novel
technical approach to reducing the sensitivity of the estimation procedure that
we call recursive private preconditioning.

本文介绍了用于两种基本高维学习问题的新型、计算有效和差分隐私算法：学习多元高斯分布和在布尔超立方体上学习乘积分布。我们的算法的样本复杂度几乎与这些任务的最优非隐私学习器的样本复杂度相匹配，表明隐私在这些问题上是几乎免费的。