Speaker-dependent audio-visual emotion recognition

By S. Haq, Philip J. B. Jackson. 2009

169 citations recorded by the source corpus.

Subfields: Multimodal Learning, Sentiment & Opinion Analysis, Speech & Audio

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In Sentiment & Opinion Analysis

In Speech & Audio

Citations in the corpus

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021, background): “The benchmark includes a variety of tasks such as speech emotion recognition [85], [86], speaker identification [87], and language identification [88], but specifically excludes tasks that are focused on the meaning of words.”
Towards Learning a Universal Non-Semantic Representation of Speech (2020, background): “SAVEE (Haq et al., 2009) is also an emotion recognition task.”
Developing crossmodal expression recognition based on a deep neural model (2016, background): “The authors of the SAVEE dataset, Haq, Jackson, and Edge (2009), also did a study to examine the human performance for the same task.”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “[Haq and Jackson 2009] 4 act disc class 7 disc basic Face + Voice dec dep”
Speech Emotion Recognition Using CNN (2014, methodology): “Our affect-salient feature learning method was evaluated on four public emotional speech databases with different languages: Surrey Audio-Visual Expressed Emotion (SAVEE) Database [7], Berlin Emotional Database (Emo-DB) [8], Danish Emotional Speech database (DES) [9], Mandarin Emotional Speech datab”
Databases, features and classifiers for speech emotion recognition: a review (2018, methodology): “The Surrey Audio-Visual Expressed Emotion (SAVEE) database (Haq and Jackson 2009) has been recorded as a pre-requisite for the development of an automatic emotion recognition system.”