Emotion recognition in human-computer interaction

By M. Hay. 2012

750 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fc926d019e454e156ad078fc52f0f5c4d42d9198

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Citations in the corpus

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English (2018, background): “Peak normalization was chosen to retain the natural variation in loudness between emotional conditions [95, 126, 133].”
Affective Body Expression Perception and Recognition: A Survey (2013, unknown): “I N recent years, there has been a growing interest in the development of technology that has the crucial ability to recognize people's affective states [1] as the role played by affect in human development and everyday functioning is now well recognized [2].”
Decorative pictures and emotional design in multimedia learning (2016, methodology): “These pictures will be changed by the facial expressions and gestures of the shown extrase a procedure that is often used to manipulate emotions (Cowie et al., 2001).”
Developing crossmodal expression recognition based on a deep neural model (2016, methodology): “This method became very popular for emotion recognition systems, as shown in the review work of Cowie et al. (2001).”
Speech Intention Classification with Multimodal Deep Learning (2017, methodology): “…and softmax configuration was trained on several widely used manufactured features including fundamental frequency [19], pitch related features [20], energy related features [21], zero crossing rate (ZCR) [21, 22], jitter [21], shimmer [21], and Mel-frequency cepstral coefficients (MFCC)…”
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities (2021, background): “Automatic multimodal emotion recognition is very important to natural human-computer interactions (Fragopanagos and Taylor, 2002).”
Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, background): “In the human–machine interaction (HMI), we also expect machines to communicate with us according to our emotions, where emotion recognition is the key problem [2].”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “Numerous innovative solutions to address some of the aforementioned challenges have been extensively reviewed in both early (prior to 2009 – see [Cowie et al. 2001; Jaimes and Sebe 2007; Pang and Lee 2008; Pantic and Rothkrantz 2003]) and more recent surveys (2009 to present – see [Calvo and D’Mello”