Emotion representation, analysis and synthesis in continuous space: A survey

Abstract

Despite major advances within the affective computing research field, modelling, analysing, interpreting and responding to naturalistic human affective behaviour still remains as a challenge for automated systems as emotions are complex constructs with fuzzy boundaries and with substantial individual variations in expression and experience. Thus, a small number of discrete categories (e.g., happiness and sadness) may not reflect the subtlety and complexity of the affective states conveyed by such rich sources of information. Therefore, affective and behavioural computing researchers have recently invested increased effort in exploring how to best model, analyse, interpret and respond to the subtlety, complexity and continuity (represented along a continuum e.g., from-1 to +1) of affective behaviour in terms of latent dimensions (e.g., arousal, power and valence) and appraisals. Accordingly, this paper aims to present the current state of the art and the new challenges in automatic, dimensional and continuous analysis and synthesis of human emotional behaviour in an interdisciplinary perspective.

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

“…They exhibit substantial individual variations in expression and experience [21]. Emotion recognition is such a challenging task that it is unlikely to achieve perfect accuracy [12].…”

“…Shorter pauses and inter-breath stretches are indicative of higher activation. Fast speaking rate, less high-frequency energy, low pitch and large pitch range and longer vowel durations are related to positive valence [21]. We extract features related to statistics of formant contours (Table 1), pitch contours (Table 2), and energy contours ( Table 3).…”

“…On the other hand, several features are investigated here for the first time for the emotion recognition task. Mean of the 1st, 2nd, 3rd, and 4th formant [5][6][7][8] Maximum of the 1st, 2nd, 3rd, and 4th formant [9][10][11][12] Minimum of the 1st, 2nd, 3rd, and 4th formant [13][14][15][16] Variance of the 1st, 2nd, 3rd, and 4th formant [17][18][19][20] Skewness of the 1st, 2nd, 3rd, and 4th formant [21][22][23][24] Interquartile range of the 1st, 2nd, 3rd, and 4th formant [25][26][27][28] Range (i.e. maximum-minimum) of the 1st, 2nd, 3rd, and 4th formant [29][30][31][32] 90th percentile of the 1st, 2nd, 3rd, and 4th formant Several observations are made by the research community with respect to the correspondence among features and emotional categories.…”

Affective Human-Humanoid Interaction Through Cognitive Architecture

“…The first approach is based on the definition of a reduced set of basic emotions, innate and universally recognized. This model is widely used in automatic recognition of emotions, but as well as for human actions and intentions, can be considered more complex models that address a continuous range of affective and emotional states (Gunes et al, 2011). Dimensional models are described by geometric spaces that can use the basic emotions, but represented by a continuous dynamic dimensions such as arousal, valence, expectation, intensity.…”

Multimodal Emotion Recognition Using Deep Networks

“…The neural networks architectures found in literature mainly consist of variants of a multilayer artificial networks [7,8].…”