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Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different [[Modality (human–computer interaction)|modalities]], such as [[speech recognition]], [[natural language processing]], or [[face recognition|facial expression detection]]. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be taught to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). These labels may or may not correspond to what the person is actually feeling.
Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different [[Modality (human–computer interaction)|modalities]], such as [[speech recognition]], [[natural language processing]], or [[face recognition|facial expression detection]]. The goal of most of these techniques is to produce labels that would match the labels a human perceiver would give in the same situation: For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be taught to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). These labels may or may not correspond to what the person is actually feeling.
识别情感信息需要从收集到的数据中提取出有意义的模式。这通常要使用'''多模态'''机器学习技术，如'''语音识别'''、'''自然语言处理'''或'''面部表情检测'''等。大多数这些技术的目标是给出与人类感情相一致的标签: 例如，如果一个人做出皱眉的面部表情，那么计算机视觉系统可能会被教导将他们的脸标记为“困惑”、“专注”或“轻微消极”(与象征着积极的快乐微笑相反)。这些标签可能与人们的真实感受相符，也可能不相符。
识别情感信息需要从收集到的数据中提取出有意义的模式。这通常要使用'''[[wikipedia:Multimodality|多模态]]'''机器学习技术，如'''语音识别'''、'''自然语言处理'''或'''面部表情检测'''等。大多数这些技术的目标是给出与人类感情相一致的标签: 例如，如果一个人做出皱眉的面部表情，那么计算机视觉系统可能会被教导将他们的脸标记为“困惑”、“专注”或“轻微消极”(与象征着积极的快乐微笑相反)。这些标签可能与人们的真实感受相符，也可能不相符。
=== 机器中的情感 ===
=== 机器中的情感 ===
[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref name=":6">{{cite news|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html|title=Mind Over Matter|last=Restak|first=Richard|date=2006-12-17|work=The Washington Post|access-date=2008-05-13}}</ref>
[[Marvin Minsky]], one of the pioneering computer scientists in [[artificial intelligence]], relates emotions to the broader issues of machine intelligence stating in ''[[The Emotion Machine]]'' that emotion is "not especially different from the processes that we call 'thinking.'"<ref name=":6">{{cite news|url=https://www.washingtonpost.com/wp-dyn/content/article/2006/12/14/AR2006121401554.html|title=Mind Over Matter|last=Restak|first=Richard|date=2006-12-17|work=The Washington Post|access-date=2008-05-13}}</ref>
人工智能领域的计算机科学先驱之一[https://zh.wikipedia.org/wiki/%E9%A9%AC%E6%96%87%C2%B7%E9%97%B5%E6%96%AF%E5%9F%BA 马文•明斯基](Marvin Minsky)在[[wikipedia:The_Emotion_Machine|《情绪机器》]](The Emotion Machine)一书中将情绪与更广泛的机器智能问题联系起来。他在书中表示，情绪“与我们所谓的‘思考’过程并没有特别的不同。'"<ref name=":6" />
== 技术 ==
== 技术 ==
Various changes in the autonomic nervous system can indirectly alter a person's speech, and affective technologies can leverage this information to recognize emotion. For example, speech produced in a state of fear, anger, or joy becomes fast, loud, and precisely enunciated, with a higher and wider range in pitch, whereas emotions such as tiredness, boredom, or sadness tend to generate slow, low-pitched, and slurred speech.<ref name=":8">Breazeal, C. and Aryananda, L. [http://web.media.mit.edu/~cynthiab/Papers/breazeal-aryananda-AutoRo02.pdf Recognition of affective communicative intent in robot-directed speech]. Autonomous Robots 12 1, 2002. pp. 83–104.</ref> Some emotions have been found to be more easily computationally identified, such as anger<ref name="Dellaert" /> or approval.<ref name=":9">{{Cite book|last1=Roy|first1=D.|last2=Pentland|first2=A.|date=1996-10-01|title=Automatic spoken affect classification and analysis|journal=Proceedings of the Second International Conference on Automatic Face and Gesture Recognition|pages=363–367|doi=10.1109/AFGR.1996.557292|isbn=978-0-8186-7713-7|s2cid=23157273}}</ref>
Various changes in the autonomic nervous system can indirectly alter a person's speech, and affective technologies can leverage this information to recognize emotion. For example, speech produced in a state of fear, anger, or joy becomes fast, loud, and precisely enunciated, with a higher and wider range in pitch, whereas emotions such as tiredness, boredom, or sadness tend to generate slow, low-pitched, and slurred speech.<ref name=":8">Breazeal, C. and Aryananda, L. [http://web.media.mit.edu/~cynthiab/Papers/breazeal-aryananda-AutoRo02.pdf Recognition of affective communicative intent in robot-directed speech]. Autonomous Robots 12 1, 2002. pp. 83–104.</ref> Some emotions have been found to be more easily computationally identified, such as anger<ref name="Dellaert" /> or approval.<ref name=":9">{{Cite book|last1=Roy|first1=D.|last2=Pentland|first2=A.|date=1996-10-01|title=Automatic spoken affect classification and analysis|journal=Proceedings of the Second International Conference on Automatic Face and Gesture Recognition|pages=363–367|doi=10.1109/AFGR.1996.557292|isbn=978-0-8186-7713-7|s2cid=23157273}}</ref>
[https://zh.wikipedia.org/zh-sg/%E8%87%AA%E4%B8%BB%E7%A5%9E%E7%BB%8F%E7%B3%BB%E7%BB%9F 自主神经系统]的各种变化可以间接地改变一个人的语言，情感技术可以利用这些信息来识别情绪。例如，在恐惧、愤怒或高兴的状态下发言变得快速、响亮、清晰，音调变得越来越高，音域越来越宽；而诸如疲倦、厌倦或悲伤等情绪往往会产生缓慢、低沉、含糊不清的语音<ref name=":8" /> 。有些情绪更容易被计算识别，比如愤怒<ref name="Dellaert" /> 或赞同<ref name=":9" />。
Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and [[prosody (linguistics)|prosodic]] features such as pitch variables and speech rate can be analyzed through pattern recognition techniques.<ref name="Dellaert">Dellaert, F., Polizin, t., and Waibel, A., Recognizing Emotion in Speech", In Proc. Of ICSLP 1996, Philadelphia, PA, pp.1970–1973, 1996</ref><ref name="Lee">Lee, C.M.; Narayanan, S.; Pieraccini, R., Recognition of Negative Emotion in the Human Speech Signals, Workshop on Auto. Speech Recognition and Understanding, Dec 2001</ref>
Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and [[prosody (linguistics)|prosodic]] features such as pitch variables and speech rate can be analyzed through pattern recognition techniques.<ref name="Dellaert">Dellaert, F., Polizin, t., and Waibel, A., Recognizing Emotion in Speech", In Proc. Of ICSLP 1996, Philadelphia, PA, pp.1970–1973, 1996</ref><ref name="Lee">Lee, C.M.; Narayanan, S.; Pieraccini, R., Recognition of Negative Emotion in the Human Speech Signals, Workshop on Auto. Speech Recognition and Understanding, Dec 2001</ref>
As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging, linear Gaussian smoothing, median filtering, or newer methods such as the Bacterial Foraging Optimization Algorithm.Clever Algorithms. "Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms" . Clever Algorithms. Retrieved 21 March 2011."Soft Computing". Soft Computing. Retrieved 18 March 2011.
As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging, linear Gaussian smoothing, median filtering, or newer methods such as the Bacterial Foraging Optimization Algorithm.Clever Algorithms. "Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms" . Clever Algorithms. Retrieved 21 March 2011."Soft Computing". Soft Computing. Retrieved 18 March 2011.
正如计算领域的多数问题一样，在面部情感检测研究中，也有很多障碍需要克服，以便充分释放算法和方法的全部潜力。在几乎所有基于人工智能的检测(语音识别、人脸识别、情感识别)的早期，建模和跟踪的准确性一直是个问题。随着硬件的发展，数据集的完善，新的发现和新的实践的引入，准确性问题逐渐被解决，留下了噪音问题。现有的去噪方法包括'''邻域平均法'''、'''线性高斯平滑法'''、'''中值滤波法'''，或者更新的方法如'''菌群优化算法'''。
正如计算领域的多数问题一样，在面部情感检测研究中，也有很多障碍需要克服，以便充分释放算法和方法的全部潜力。在几乎所有基于人工智能的检测(语音识别、人脸识别、情感识别)的早期，建模和跟踪的准确性一直是个问题。随着硬件的发展，数据集的完善，新的发现和新的实践的引入，准确性问题逐渐被解决，留下了噪音问题。现有的去噪方法包括'''[https://baike.baidu.com/item/%E7%9B%B8%E9%82%BB%E5%B9%B3%E5%9D%87%E6%B3%95/9807406 邻域平均法]'''、'''线性高斯平滑法'''、'''[https://zh.wikipedia.org/wiki/%E4%B8%AD%E5%80%BC%E6%BB%A4%E6%B3%A2%E5%99%A8 中值滤波法]'''，或者更新的方法如'''菌群优化算法'''。
Other challenges include
Other challenges include