On the Evolution of Speech Representations for Affective Computing: A brief history and critical overview

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On the Evolution of Speech Representations for Affective Computing: A brief history and critical overview

By: 
Sina Alisamir; Fabien Ringeval

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.

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.

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