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The Wonderful World of Affective Computing: How Can Machines Read Our Emotions?
With the continuous advancement of technology, affective computing (Affective Computing) has become a research field with great potential. This interdisciplinary field focuses on developing systems and devices that can recognize, interpret, process and simulate human emotions. The origins of affective computing can be traced to early philosophical discussions of emotion, while modern developments began with a 1995 paper by Rosalind Picard and a 1997 book of the same name. It’s a process that aims to give machines emotional intelligence, one of which is simulating empathy so that machines can understand human emotional states and respond appropriately.
The core of affective computing is how to allow machines to better understand human emotions and adapt to data-driven interactions.
Emotion recognition technology
The process of emotion recognition usually starts with data capture from passive sensors that capture the user's physiological state or behavior without interpreting the input. This data is similar to the cues humans use when understanding the emotions of others. For example, video cameras can capture facial expressions, body postures and gestures, while microphones can capture speech. Not only that, but other sensors can detect emotional cues by directly measuring physiological data such as skin temperature and electrocutaneous response.
Based on data analysis techniques, these emotional features are eventually labeled, such as facial expressions labeled "confused" or "happy."
Emotion in the machine
Another area of affective computing is designing computing devices that can exhibit emotions or that can successfully simulate emotions. Current technological capabilities make simulating emotions through conversational agents a practical application. Marvin Minsky once pointed out that emotion is related to the overall problem of machine intelligence, and mentioned in "The Emotion Machine" that emotion and "thinking" are integrated with each other.
The innovative design of digital humans attempts to give these simulated human programs an emotional dimension, allowing them to respond accordingly in emotionally stimulating situations.
Emotional speech analysis
In the development of affective computing, the emotional analysis of speech is particularly important. Emotion recognition technology can determine the user's emotional state through computational analysis of speech features. Research shows that fast, loud, and clear speech is often associated with emotions such as fear, anger, or joy, while slow, deep, and slurred speech is more often associated with burnout, boredom, or sadness. In addition, the calculation accuracy of speech features can reach about 70% to 80%, exceeding the average human accuracy (about 60%).
Challenges of sentiment analysis
Although a variety of emotion recognition technologies have been developed, they still face many challenges. For example, the emotions shown by actors are often different from the emotions shown in real life. Furthermore, emotional accuracy was found to decrease when facial posture was changed. Since emotion is a dynamic process, it is difficult to conduct accurate analysis statically. This requires us to consider not only various input data but also the complexity of the situation in affective computing technology.
Artificial intelligence emotion detection needs to be supported by multi-modal information to improve the accuracy of recognition.
Future Outlook
With the development of technology, the application potential of affective computing is huge. Not only will we be able to equip machines with a deeper emotional understanding, but we will also be able to make human-machine interaction more natural. However, with the development of affective computing, we also need to reflect: Can machines truly understand human emotions? Will such technology change our understanding of emotion?
