Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.

/pdf/the-ryerson-audio-visual-database-of-emotional-speech-and-jx71tsx3ts.pdf

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology.

A survey of affect recognition methods: audio, visual and spontaneous expressions

http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2011/Schuller11-RRE.pdf

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

People convey their emotional state in their face and voice. We present an audio-visual dataset uniquely suited for the study of multi-modal emotion expression and perception. The dataset consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnicbackgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels andreal-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9, 58.2 and 63.6 percent respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest forvisual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger andhappiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.

/pdf/crema-d-crowd-sourced-emotional-multimodal-actors-dataset-5egzc5gowo.pdf

CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset

Automatic detection of emotions has been evaluated using standard Mel-frequency Cepstral Coefficients, MFCCs, and a variant, MFCC-low, calculated between 20 and 300 Hz, in order to model pitch. Also plain pitch features have been used. These acoustic features have all been modeled by Gaussian mixture models, GMMs, on the frame level. The method has been tested on two different corpora and languages; Swedish voice controlled telephone services and English meetings. The results indicate that using GMMs on the frame level is a feasible technique for emotion classification. The two MFCC methods have similar performance, and MFCC-low outperforms the pitch features. Combining the three classifiers significantly improves performance.

/pdf/emotion-recognition-in-spontaneous-speech-using-gmms-5aqgqero84.pdf

Emotion Recognition in Spontaneous Speech Using GMMs

The possibility of cultural differences in the fundamental acoustic patterns used to express emotion through the voice is an unanswered question central to the larger debate about the universality versus cultural specificity of emotion. This study used emotionally inflected standard-content speech segments expressing 11 emotions produced by 100 professional actors from 5 English-speaking cultures. Machine learning simulations were employed to classify expressions based on their acoustic features, using conditions where training and testing were conducted on stimuli coming from either the same or different cultures. A wide range of emotions were classified with above-chance accuracy in cross-cultural conditions, suggesting vocal expressions share important characteristics across cultures. However, classification showed an in-group advantage with higher accuracy in within- versus cross-cultural conditions. This finding demonstrates cultural differences in expressive vocal style, and supports the dialect theory of emotions according to which greater recognition of expressions from in-group members results from greater familiarity with culturally specific expressive styles.

Evidence for cultural dialects in vocal emotion expression: acoustic classification within and across five nations.

This paper presents our progress in developing a Virtual Human capable of being an attentive speaker. Such a Virtual Human should be able to attend to its interaction partner while it is speaking—and modify its communicative behavior on-the-fly based on what it observes in the behavior of its partner. We report new developments concerning a number of aspects, such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and strategies for generating appropriate reactions to listener responses. On the basis of this progress, a task-based setup for a responsive Virtual Human was implemented to carry out two user studies, the results of which are presented and discussed in this paper.

/pdf/continuous-interaction-with-a-virtual-human-3y90pha9bv.pdf

Continuous Interaction with a Virtual Human

Automatic detection of real life negative emotions in speech has been evaluated using Linear Discriminant Analysis, LDA, with "classic" emotion features and a classifier based on Gaussian Mixture M 

/pdf/automatic-recognition-of-anger-in-spontaneous-speech-3hifxqtw2t.pdf

Automatic Recognition of Anger in Spontaneous Speech

This paper studies the hypothesis that the acoustic-to-articulatory mapping is non-unique, statistically. The distributions of the acoustic and articulatory spaces are obtained by fitting the data into a Gaussian Mixture Model. The kurtosis is used to measure the non-Gaussianity of the distributions and the Bhattacharya distance is used to find the difference between distributions of the acoustic vectors producing non-unique articulator configurations. It is found that stop consonants and alveolar fricatives arc generally not only non-linear but also non-unique, while dental fricatives arc found to be highly non-linear but fairly unique. Two more investigations are also discussed: the first is on how well the best possible piecewise linear regression is likely to perform, the second is on whether the dynamic constraints improve the ability to predict different articulatory regions corresponding to the same region in the acoustic space.

/pdf/the-acoustic-to-articulation-mapping-non-linear-or-non-63karsgnob.pdf

The acoustic to articulation mapping: non-linear or non-unique?

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