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In this study, the robustness of approaches to the automatic classification of emotions in speech is addressed. Among the many types of emotions that exist, two groups of emotions are considered, adult-to-adult acted vocal expressions of common types of emotions like happiness, sadness, and anger and adult-to-infant vocal expressions of affective intents also known as "motherese". Specifically, we estimate the generalization capability of two feature extraction approaches, the approach developed for Sony's robotic dog AIBO (AIBO) and the segment-based approach (SBA) of . Three machine learning approaches are considered, K-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Adaboosted decision trees and four emotional speech databases are employed, Kismet, BabyEars, Danish, and Berlin databases.Single corpus experiments show that the considered feature extraction approaches AIBO and SBA are competitive on the four databases considered and that their performance is comparable with previously published results on the same databases. The best choice of machine learning algorithm seems to depend on the feature extraction approach considered.Multi corpus experiments are performed with the Kismet-BabyEars and the Danish-Berlin database pairs that contain parallel emotional classes. Automatic clustering of the emotional classes in the database pairs shows that the patterns behind the emotions in the Kismet-BabyEars pair are less database dependent than the patterns in the Danish-Berlin pair. In off-corpus testing the classifier is trained on one database of a pair and tested on the other. This provides little improvement over baseline classification. In integrated corpus testing, however, the classifier is machine learned on the merged databases and this gives promisingly robust classification results, which suggest that emotional corpora with parallel emotion classes recorded under different conditions can be used to construct a single classifier capable of distinguishing the emotions in the merged corpora. Such a classifier is more robust than a classifier learned on a single corpus as it can recognize more varied expressions of the same emotional classes. These findings suggest that the existing approaches for the classification of emotions in speech are efficient enough to handle larger amounts of training data without any reduction in classification accuracy.

This study reports the occurrence of 'tonal synchrony' as a new dimension of early mother-infant interaction synchrony. The findings are based on a tonal and temporal analysis of vocal interactions between 15 mothers and their 3-month-old infants during 5 min of free-play in a laboratory setting. In total, 558 vocal exchanges were identified and analysed, of which 84% reflected harmonic or pentatonic series. Another 10% of the exchanges contained absolute and/or relative pitch and/or interval imitations. The total durations of dyads being in tonal synchrony were normally distributed (M=3.71, SD=2.44). Vocalisations based on harmonic series appeared organised around the major triad, containing significantly more simple frequency ratios (octave, fifth and third) than complex ones (non-major triad tones). Tonal synchrony and its characteristics are discussed in relation to infant-directed speech, communicative musicality, pre-reflective communication and its impact on the quality of early mother-infant interaction and child's development.

Abstract-Nowadays robots are being created that interact with human beings in order to satisfy certain social needs. Following this trend, the development of the social robot Probo has started. The robot will be used in hospitals, as a tele-interface for entertainment, communication and medical assistance. Therefore, it requires the ability to express emotions. In order to do so, an emotional interface is developed to fully configure the display of emotions. These emotions -represented as a vector in an emotion space-are mapped to the degrees of freedom used in the robot. Besides emotions, the interface includes a control for the point of attention and a module to create and store animations. A 3D virtual model is created, acting as a virtual replica of the robot, providing realistic visual feedback to evaluate the design choices for the facial expressions. This paper presents the objectives of this new robot and describe the concepts and design of the first prototype.Index Terms-Human robot interaction, robot assisted therapy, robot design.

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