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Russian Multimodal Corpus of Dyadic Interaction for Studying Emotion Recognition RAMAS: Russian Multimodal Corpus of Dyadic Interaction for Studying Emotion Recognition Olga Perepelkina Eva Kazimirova Maria Konstantinova Neurodata Lab LLC Neurodata Lab LLC Neurodata Lab LLC Lomonosov MSU Moscow, Russia Lomonosov MSU Moscow, Russia [email protected] Moscow, Russia [email protected] [email protected] Abstract—Emotion expression encompasses various types of The investigations of facial expression recognition can be information, including face and eye movement, voice and body classified into two parts, the detection of facial affect (human motion. Most of the studies in automated affective recognition emotions), and the detection of facial muscle action (action use faces as stimuli, less often they include speech and even more rarely gestures. Emotions collected from real conversations are units) [4]. Currently a good classification accuracy (more difficult to classify using one channel. That is why multimodal than 90% [5, 6]) of basic emotions has been achieved by techniques have recently become more popular in automatic using face images. Speech is another essential channel for emotion recognition. Multimodal databases that include audio, emotion recognition. Some emotions, such as sadness and video, 3D motion capture and physiology data are quite rare. fear, could be distinguished from an audio stream even better We collected The Russian Acted Multimodal Affective Set (RA- MAS) the first multimodal corpus in Russian language. Our than from video [7]. The average recognition level across database contains approximately 7 hours of high-quality close- different studies varies from 45% to 90% and depends on up video recordings of subjects faces, speech, motion-capture the number of categories, the classifier type and the dataset data and such physiological signals as electro-dermal activity and type [8]. Physiological signals like cardiovascular, respiratory photoplethysmogram. The subjects were 10 actors who played and electrodermal measures are also successfully applied in out interactive dyadic scenarios. Each scenario involved one of the basic emotions: Anger, Sadness, Disgust, Happiness, Fear emotion recognition. In several studies of biosignal-based or Surprise, and some characteristics of social interaction like affect classification recognition rate is more than 80% [9, 10]. Domination and Submission. In order to note emotions that The analysis of body motion data for emotion recognition has subjects really felt during the process we asked them to fill in become more common only recently. Movement data gives short questionnaires (self-reports) after each played scenario. The recognition rates comparable to facial expressions or speech records were marked by 21 annotators (at least five annotators marked each scenario). We present our multimodal data col- in multimodal scenarios, and improves overall accuracy in lection, annotation process, inter-rater agreement analysis and multimodal systems when combined with other modalities [2]. the comparison between self-reports and received annotations. Emotions collected from real conversations are difficult to RAMAS is an open database that provides research community classify by means of one channel. That is why multimodal with multimodal data of faces, speech, gestures and physiology techniques have recently become more popular in automatic interrelation. Such material is useful for various investigations and automatic affective systems development. emotion recognition. Multimodal databases containing audio- Index Terms—affective computing, multimodal affect recogni- visual information and 3D motion capture are quite rare. The tion, multimodal dataset USC CreativeIT database [11] may serve as an example here. It includes full-body motion capture, video and audio data. This I. INTRODUCTION database provides annotations for each fragment concerning There are several approaches to classification and cate- valence, activation and dominance categories. The interactive gorization of affective conditions. Most studies use discrete emotional dyadic motion capture database (IEMOCAP) [12] categories (e.g. basic emotions such as happiness and sadness). contains audio-visual and motion data for faces and hands However, a few studies focus on dimensional models (such as only, but not for the whole body. The MPI Emotional Body valence and arousal) [1]. Dimensional approach in automated Expressions Database [13] was also collected by means of emotion recognition faces challenges such as unbalanced data, several channels, yet only motion capture data are available overlapping categories and differences in the inter-observer for the research community. Finally, there are well-designed agreement on the dimensions [1, 2]. On the other hand, multimodal databases (e.g. RECOLA dataset [14], GEMEP categorical labels describe emotional states based on their corpus [15]) that provide multichannel information, but leave linguistic use in our daily life. Most studies use a basic set out motion data. Thus, we decided to collect multimodal of emotions that includes anger, happiness, sadness, surprise, database with multiple channels (video, audio, motion and disgust and fear [3]. At present time there are many studies physiology data) in Russian, since existing datasets have of automatic emotion recognition using one modality. The some limitations. RAMAS expands and complements existing most popular source for affect recognition is the face data. datasets for the needs of affective computing. PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.26688v1 | CC BY 4.0 Open Access | rec: 13 Mar 2018, publ: 13 Mar 2018 II. RAMAS DATASET skeleton data, RGB and depth video, EDA, and PPG. We used A. Dataset collection SSI software to synchronize the streams [21]. The Russian Acted Multimodal Affective Set (RAMAS) III. POST-PROCESSING AND ANNOTATION consists of multimodal recordings of improvised affective Statistical analysis was performed using R 3.4.2 (R Studio dyadic interactions in Russian. It was obtained in 2016-2017 version 1.1.383). by Neurodata Lab LLC [16]. Ten semi-professional actors (5 A. Annotation men and 5 women, 18-28 years old, native Russian speakers) participated in the data collection. Semi-professional actors We asked 21 annotators (18-33 years old, 71% women) to are more preferable than professional actors for analyzing evaluate emotions in the received video-audio fragments. Each movements in emotional states, as professional theatre actors annotator worked with 150 video fragments (except for two may use stereotypical motion patterns [13, 17]. annotators who had 150 fragments in sum) and at least five The actors were given scenarios with descriptions of dif- annotators marked each fragment. We asked all the applicants ferent situations, but no exact lines. They were encouraged to take the emotional intelligence test [22, 23], and only those, to gesticulate and move, but they had to stay in a certain, who got average or above the average results, were picked to specially marked part of the room to achieve stable close- annotate the material. The Elan [24] tool from Max Planck up footage of their faces. In order to perform refined motion Institute for Psycholinguistics (the Netherlands) was used for tracking all the actors were dressed in tight black clothes. First emotion annotation. Oral and written instructions as well as of all the participants contributed a sample of their neutral templates of all the emotional states were provided. The task emotional state. Then they improvised from 30 to 60 seconds was to mark the beginning and the end of each emotion that on each of the 13 topics. Interactions were conducted in mixed- seemed natural. The work of the annotators was monitored gender dyads where participants were assigned to be either and paid for. friends or colleagues. Scenarios implied the presence of one B. Inter-Annotator Agreement of the six basic emotions (Angry, Sad, Disgusted, Happy, Scared, and Surprised) and the neutral state in each dialogue. We used Krippendorff’s alpha statistic [25] to estimate the There were dominated and submissive roles in each scenario amount of inter-rater agreement in the RAMAS database. that were balanced across all scenarios. English translations Alpha defines the two reliability scale points as 1.0 for perfect of sceanarios could be found under the link [18]. The actors reliability, 0.0 for the absence of reliability, and alpha < 0 were coordinated by a professional teacher from Russian State when disagreements are systematic and exceed what can be University of Cinematography named after S. Gerasimov. Each expected by chance. We choose Krippendorffs alpha because scenario was played out from two to five times to achieve it is applicable to any number of coders, allows for reliability the best quality and the highest variety in emotional and data with missing categories or scale points, and measures behavior expressions. The roles were assigned to actors in agreements for nominal, ordinal, interval, and ratio data [26]. such a way that all states were evenly distributed between The expression for calculation of Alpha (α) is as follows: men and women. We also asked the subjects to fill in short 1 − D α = 0 (1) questionnaires (self-reports) after each played scenario in order De to note emotions they really felt during the process. The where D0 is the disagreement observed and De is the disagree- actors received fixed payments for the production days and ment expected by chance. consented to
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