Cross-corpus speech emotion recognition has recently received considerable attention due to the widespread existence of various emotional speech. It takes one corpus as the training data aiming to recognize emotions of another corpus, and generally involves two basic problems, i.e., feature matching and feature selection. Many previous works study these two problems independently, or just focus on solving the first problem. In this paper, we propose a novel algorithm, called feature selection based transfer subspace learning (FSTSL), to address these two problems. To deal with the first problem, a latent common subspace is learnt by reducing the difference of different corpora and preserving the important properties. Meanwhile, we adopt the $l_{2,1}$l2,1-norm on the projection matrix to deal with the second problem. Besides, to guarantee the subspace to be robust and discriminative, the geometric information of data is exploited simultaneously in the proposed FSTSL framework. Empirical experiments on cross-corpus speech emotion recognition tasks demonstrate that our proposed method can achieve encouraging results in comparison with state-of-the-art algorithms.

中文翻译：

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基于特征选择的语音情感识别迁移子空间学习

由于各种情感语音的广泛存在，跨语料语音情感识别最近受到了相当大的关注。它以一个语料库为训练数据，旨在识别另一个语料库的情绪，一般涉及两个基本问题，即特征匹配和特征选择。以前的很多作品都是独立研究这两个问题的，或者只是专注于解决第一个问题。在本文中，我们提出了一种新算法，称为基于特征选择的转移子空间学习（FSTSL），以解决这两个问题。为了解决第一个问题，通过减少不同语料库的差异并保留重要属性来学习潜在的公共子空间。同时，我们采用$l_{2,1}$升2,1-norm 在投影矩阵上处理第二个问题。此外，为了保证子空间的鲁棒性和判别性，在所提出的 FSTSL 框架中同时利用了数据的几何信息。跨语料库语音情感识别任务的实证实验表明，与最先进的算法相比，我们提出的方法可以取得令人鼓舞的结果。