Multimodal Subspace Support Vector Data Description

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary material (27 tables, 10 figures). The manuscript and supplementary material are combined as a single .pdf (50 pages) fil

New Tests to Measure Individual Differences in Matching and Labelling Facial Expressions of Emotion, and Their Association with Ability to Recognise Vocal Emotions and Facial Identity

Although good tests are available for diagnosing clinical impairments in face expression processing, there is a lack of strong tests for assessing "individual differences"--that is, differences in ability between individuals within the typical, nonclinical, range. Here, we develop two new tests, one for expression perception (an odd-man-out matching task in which participants select which one of three faces displays a different expression) and one additionally requiring explicit identification of the emotion (a labelling task in which participants select one of six verbal labels). We demonstrate validity (careful check of individual items, large inversion effects, independence from nonverbal IQ, convergent validity with a previous labelling task), reliability (Cronbach's alphas of.77 and.76 respectively), and wide individual differences across the typical population. We then demonstrate the usefulness of the tests by addressing theoretical questions regarding the structure of face processing, specifically the extent to which the following processes are common or distinct: (a) perceptual matching and explicit labelling of expression (modest correlation between matching and labelling supported partial independence); (b) judgement of expressions from faces and voices (results argued labelling tasks tap into a multi-modal system, while matching tasks tap distinct perceptual processes); and (c) expression and identity processing (results argued for a common first step of perceptual processing for expression and identity).This research was supported by the Australian Research Council (http://www.arc.gov.au/) grant DP110100850 to RP and EM and the Australian Research Council Centre of Excellence for Cognition and its Disorders (CE110001021) http://www.ccd.edu.au. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier

The paper is aimed at determination of formant features (FF) which describe vocal tract characteristics. It comprises analysis of the first three formant positions together with their bandwidths and the formant tilts. Subsequently, the statistical evaluation and comparison of the FF was performed. This experiment was realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. The statistical distribution of the analyzed formant frequencies and formant tilts shows good differentiation between neutral and emotional styles for both voices. Contrary to it, the values of the formant 3-dB bandwidths have no correlation with the type of the speaking style or the type of the voice. These spectral parameters together with the values of the other speech characteristics were used in the feature vector for Gaussian mixture models (GMM) emotional speech style classifier that is currently developed. The overall mean classification error rate achieves about 18 %, and the best obtained error rate is 5 % for the sadness style of the female voice. These values are acceptable in this first stage of development of the GMM classifier that should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation

Affect recognition from face and body: Early fusion vs. late fusion

This paper presents an approach to automatic visual emotion recognition from two modalities: face and body. Firstly, individual classifiers are trained from individual modalities. Secondly, we fuse facial expression and affective body gesture information first at a feature-level, in which the data from both modalities are combined before classification, and later at a decision-level, in which we integrate the outputs of the monomodal systems by the use of suitable criteria. We then evaluate these two fusion approaches, in terms of performance over monomodal emotion recognition based on facial expression modality only. In the experiments performed the emotion classification using the two modalities achieved a better recognition accuracy outperforming the classification using the individual facial modality. Moreover, fusion at the feature-level proved better recognition than fusion at the decision-level. © 2005 IEEE

Multimodal approach for emotion recognition based on simulated flight experiments

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.info:eu-repo/semantics/publishedVersio