Recent advances in the field of machine learning have shown great potential for the automatic recognition of apparent human emotions. In the era of Internet of Things and big-data processing, where voice-based systems are well established, opportunities to leverage cutting-edge technologies to develop personalized and human-centered services are genuinely real, with a growing demand in many areas such as education, health, well-being, and entertainment. Automatic emotion recognition from speech, which is a key element for developing personalized and human-centered services, has reached a degree of maturity that makes it of broad commercial interest today. However, there are still major limiting factors that prevent a broad applicability of emotion recognition technology. For example, one open challenge is the poor generalization capabilities of currently used feature extraction techniques to interpret expressions of affect across different persons, contexts, cultures, and languages.

中文翻译：

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情感计算语音表示的演变：简史和批判性概述


机器学习领域的最新进展显示出自动识别人类明显情绪的巨大潜力。在物联网和大数据处理时代，基于语音的系统已经成熟，利用尖端技术开发个性化和以人为本的服务的机会确实存在，许多领域的需求不断增长，例如教育、健康、福祉和娱乐。语音自动情感识别是开发个性化和以人为本的服务的关键要素，它已经达到了一定程度的成熟度，使其在当今具有广泛的商业利益。然而，仍然存在阻碍情感识别技术广泛应用的主要限制因素。例如，一项公开的挑战是当前使用的特征提取技术在解释不同人、背景、文化和语言之间的情感表达时泛化能力较差。