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Improvised acting is a viable technique to study expressive human communication and to shed light into actors' creativity. The USC CreativeIT database provides a novel, freely-available multimodal resource for the study of theatrical improvisation and rich expressive human behavior (speech and body language) in dyadic interactions. The theoretical design of the database is based on the well-established improvisation technique of Active Analysis in order to provide naturally induced affective and expressive, goal-driven interactions. This database contains dyadic theatrical improvisations performed by 16 actors, providing detailed full body motion capture data and audio data of each participant in an interaction. The carefully engineered data collection, the improvisation design to elicit natural emotions and expressive speech and body language, as well as the well-developed annotation processes provide a gateway to study and model various aspects of theatrical performance, expressive behaviors and human communication and interaction. [ABSTRACT FROM AUTHOR]

Highlights: [•] An exhaustive analysis of various acoustic features to identify their dependencies on lexical, speaker and emotional aspects. [•] A novel trajectory-based normalization scheme to compensate for speaker or lexical variabilities. [•] Classification experiments that validate the proposed normalization approach on the IEMOCAP database. [Copyright &y& Elsevier]

Highlights: [•] We propose neutral reference models to detect emotions in the fundamental frequency. [•] A novel scheme based on functional data analysis is presented. [•] The proposed approach achieves higher accuracies than the state-of-the-art method. [•] The scheme is also employed to the to detect the most emotionally salient segments. [•] The approach is validated at the sub-sentence level by employing a natural database. [Copyright &y& Elsevier]

During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than "analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the pitch features (baseline 50%). When compared to conventional classification schemes, the proposed approach performs better in terms of both accuracy and robustness. [ABSTRACT FROM AUTHOR]

The verbal and nonverbal channels of human communication are internally and intricately connected. As a result, gestures and speech present high levels of correlation and coordination. This relationship is greatly affected by the linguistic and emotional content of the message. The present paper investigates the influence of articulation and emotions on the interrelation between facial gestures and speech. The analyses are based on an audio-visual database recorded from an actress with markers attached to her face, who was asked to read semantically neutral sentences, expressing four emotion states (neutral, sadness, happiness, and anger). A multilinear regression framework is used to estimate facial features from acoustic speech parameters. The levels of coupling between the communication channels are quantified by using Pearson's correlation between the recorded and estimated facial features. The results show that facial and acoustic features are strongly interrelated, showing levels of correlation higher than r = 0.8 when the mapping is computed at sentence-level using spectral envelope speech features. The results reveal that the lower face region provides the highest activeness and correlation levels. Furthermore, the correlation levels present significant interemotional differences, which suggest that emotional content affect the relationship between facial gestures and speech. Principal component analysis (PCA) shows that the audiovisual mapping parameters are grouped in a smaller subspace, which suggests that there is an emotion-dependent structure that is preserved from across sentences. The results suggest that this internal structure seems to be easy to model when prosodic-features are used to estimate the audiovisual mapping. The results also reveal that the correlation levels within a sentence vary according to broad phonetic properties presented in the sentence. Consonants, especially unvoiced and fricative sounds, present the lowest correlation levels. Likewise, the results show that facial gestures are linked at different resolutions. While the orofacial area is locally connected with the speech, other facial gestures such as eyebrow motion are linked only at the sentence-level. The results presented here have important implications for applications such as facial animation and multimodal emotion recognition. [ABSTRACT FROM AUTHOR]

Abstract: Automated emotion state tracking is a crucial element in the computational study of human communication behaviors. It is important to design robust and reliable emotion recognition systems that are suitable for real-world applications both to enhance analytical abilities to support human decision making and to design human–machine interfaces that facilitate efficient communication. We introduce a hierarchical computational structure to recognize emotions. The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications. The key idea is that the levels in the tree are designed to solve the easiest classification tasks first, allowing us to mitigate error propagation. We evaluated the classification framework on two different emotional databases using acoustic features, the AIBO database and the USC IEMOCAP database. In the case of the AIBO database, we obtain a balanced recall on each of the individual emotion classes using this hierarchical structure. The performance measure of the average unweighted recall on the evaluation data set improves by 3.37% absolute (8.82% relative) over a Support Vector Machine baseline model. In the USC IEMOCAP database, we obtain an absolute improvement of 7.44% (14.58%) over a baseline Support Vector Machine modeling. The results demonstrate that the presented hierarchical approach is effective for classifying emotional utterances in multiple database contexts. [Copyright &y& Elsevier]