Results 211 to 220 of about 71,619 (264)
Sex-dependent cortico-amygdala circuits controlling emotion recognition
González-Parra JA +11 more
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The Recognition of Emotion [PDF]
To detect emotional user behavior, particularly anger, can be very useful for successful automatic dialog processing. We present databases and prosodic classifiers implemented for the recognition of emotion in Verbmobil. Using a prosodic feature vector alone is, however, not sufficient for the modelling of emotional user behavior.
Batliner, Anton +5 more
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Emotion Recognition in Context
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017Understanding what a person is experiencing from her frame of reference is essential in our everyday life. For this reason, one can think that machines with this type of ability would interact better with people. However, there are no current systems capable of understanding in detail peoples emotional states.
Ronak Kosti +3 more
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Cerebellum and Emotion Recognition
2022In this chapter, after having clarified which definition of emotion we followed, starting from Darwin and evolutionary psychology, we tried to examine the main mechanisms of emotional recognition from a behavioral and cerebral point of view: emotional contagion and cognitive empathy. The link between these skills and social cognition has been discussed.
D'Agata, Federico, Orsi, Laura
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Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), 2002
This paper describes the use of statistical techniques and hidden Markov models (HMM) in the recognition of emotions. The method aims to classify 6 basic emotions (anger, dislike, fear, happiness, sadness and surprise) from both facial expressions (video) and emotional speech (audio). The emotions of 2 human subjects were recorded and analyzed.
Liyanage C. De Silva, Pei Chi Ng
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This paper describes the use of statistical techniques and hidden Markov models (HMM) in the recognition of emotions. The method aims to classify 6 basic emotions (anger, dislike, fear, happiness, sadness and surprise) from both facial expressions (video) and emotional speech (audio). The emotions of 2 human subjects were recorded and analyzed.
Liyanage C. De Silva, Pei Chi Ng
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017
Distant emotion recognition (DER) extends the application of speech emotion recognition to the very challenging situation that is determined by variable speaker to microphone distances. The performance of conventional emotion recognition systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker.
Asif Salekin +7 more
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Distant emotion recognition (DER) extends the application of speech emotion recognition to the very challenging situation that is determined by variable speaker to microphone distances. The performance of conventional emotion recognition systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker.
Asif Salekin +7 more
openaire +1 more source
International Journal for Research in Applied Science and Engineering Technology, 2022
Abstract: In this project emotion detection using its facial expressions are going to be detected. These expressions are often derived from the live feed via system’s camera or any pre-existing image available within the memory. Emotions possessed by humans will be recognized and contains a vast scope of study within the computer vision industry upon ...
Anurag Srivastava +4 more
openaire +2 more sources
Abstract: In this project emotion detection using its facial expressions are going to be detected. These expressions are often derived from the live feed via system’s camera or any pre-existing image available within the memory. Emotions possessed by humans will be recognized and contains a vast scope of study within the computer vision industry upon ...
Anurag Srivastava +4 more
openaire +2 more sources
Incremental emotion recognition
Interspeech 2013, 2013Most emotion recognition systems do not perform real-time emotion recognition due to latencies caused by phrase segmentation and resource-intensive feature acquisition, etc. To address this issue, we present an emotion recognition approach that can estimate speaker emotions with much lower latency.
Taniya Mishra, Dimitrios Dimitriadis
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