The production and recognition of emotions in speech: features and algorithms

By Pierre-Yves Oudeyer. Int. J. Hum. Comput. Stud., 2003

392 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/8b8c5a30e06da2bfa415224511a31c63ddb5a23a

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Citations in the corpus

Survey on speech emotion recognition: Features, classification schemes, and databases (2011, background): “This result is opposed to that established in [101] where it is concluded that the first and third”
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses (2015, methodology): “Approaches to predict affect from multimedia can be categorized as (i) content-centric [10], [32], using primitive audio-visual features which cannot adequately characterize the emotion perceived by the viewer, or (ii) user-centric, employing facial expressions [28] and speech intonations [26], whic”
Human computing and machine understanding of human behavior: a survey (2006, background): “The existing body of literature in machine analysis of human affect is immense [44], [40], [31].”
Expressive TTS Training With Frame and Style Reconstruction Loss (2020, background): “angry, and sad, and continuous attributes, such as activation, valence and dominance [84], [85].”
Emotion recognition from speech: a review (2012, background): “Conversation with robotic pets and humanoid partners would be more realistic and enjoyable, if they are able to understand and express emotions like humans do (Oudeyer 2003).”
Multimodal Human Computer Interaction: A Survey (2005, background): “Traditional as well as most recent studies on emotional contents in speech (see [119], [109], [72], and [155]) use “prosodic” information, that is information on intonation, rhythm, lexical stress, and other features in speech.”
Human-Computer Interaction: Overview on State of the Art (2008, neutral): “Recent endeavors to integrate human emotions in intelligent human computer interaction initiated the efforts in analysis of emotions in audio signals [48] [49].”
Primitives-based evaluation and estimation of emotions in speech (2007, neutral): “They all treat the emotion recognition problem as a multiple classiﬁcation task of several emotional categories such as angry, happy, and sad ; or simply, negative and non-negative .”
Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “In literature, various classifiers have been investigated to develop systems such as SER, speech recognition, and speaker verification, to name a few [36]–[39].”
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text (2019, background): “Researchers have achieved good results on image based emotion recognition (Wang et al., 2018), (Zhang et al., 2016) as well as voice based emotion recognition (Pierre-Yves, 2003).”
Predicting Student Emotions in Computer-Human Tutoring Dialogues (2004, background): “Prior studies (Oudeyer, 2002; Lee et al., 2002) have shown that “subject” and “gender” can play an important role in emotion recognition.”
Modeling naturalistic affective states via facial and vocal expressions recognition (2006, background): “All information related to emotion that one can extract from pitch is probably not only in these features, but the motivation of this approach is in the desire to develop and use a high level of speech prosody analysis, calculate as many features as possible and then reduce them to those uncorrelate”