Cross-cultural Emotion Recognition Research Articles

Cross-Cultural Emotion Recognition With EEG and Eye Movement Signals Based on Multiple Stacked Broad Learning System

Cross-Cultural Emotion Recognition With EEG and Eye Movement Signals Based on Multiple Stacked Broad Learning System

Comparison and Analysis of Acoustic Features of Western and Chinese Classical Music Emotion Recognition Based on V-A Model

Music emotion recognition is increasingly becoming important in scientific research and practical applications. Due to the differences in musical characteristics between Western and Chinese classical music, it is necessary to investigate the distinctions in music emotional feature sets to improve the accuracy of cross-cultural emotion recognition models. Therefore, a comparative study on emotion recognition in Chinese and Western classical music was conducted. Using the V-A model as an emotional perception model, approximately 1000 pieces of Western and Chinese classical excerpts in total were selected, and approximately 20-dimension feature sets for different emotional dimensions of different datasets were finally extracted. We considered different kinds of algorithms at each step of the training process, from pre-processing to feature selection and regression model selection. The results reveal that the combination of MaxAbsScaler pre-processing and the wrapper method using the recursive feature elimination algorithm based on extremely randomized trees is the optimal algorithm. The harmonic change detection function is a culturally universal feature, whereas spectral flux is a culturally specific feature for Chinese classical music. It is also found that pitch features are more significant for Western classical music, whereas loudness and rhythm features are more significant for Chinese classical music.

Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People

Objective. Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective. Approach. Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition. Main results. From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that the γ and β bands exhibit decreasing activities for Chinese, while for German and French, θ and α bands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures. Significance. Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.

Cross-Cultural Emotion Recognition and In-Group Advantage in Vocal Expression: A Meta-Analysis

Most research on cross-cultural emotion recognition has focused on facial expressions. To integrate the body of evidence on vocal expression, we present a meta-analysis of 37 cross-cultural studies of emotion recognition from speech prosody and nonlinguistic vocalizations, including expressers from 26 cultural groups and perceivers from 44 different cultures. Results showed that a wide variety of positive and negative emotions could be recognized with above-chance accuracy in cross-cultural conditions. However, there was also evidence for in-group advantage with higher accuracy in within- versus cross-cultural conditions. The distance between expresser and perceiver culture, measured via Hofstede’s cultural dimensions, was negatively correlated with recognition accuracy and positively correlated with in-group advantage. Results are discussed in relation to the dialect theory of emotion.

Speech Emotion Recognition: Humans vs Machines

Introduction.The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources.The typical “big six” classification commonly used in technical applications is chosen and modified to include such emotions as disgust and shame. A database of emotional speech in Russian is created under sound laboratory conditions. A perception experiment is run using Praat software’s experimental environment.Results and discussion. Cross-cultural emotion recognition possibilities are revealed, as the Finnish and international participants recognised about a half of samples correctly. Nonetheless, native speakers of Russian appear to distinguish a larger proportion of emotions correctly. The effects of foreign languages knowledge, musical training and gender on the performance in the experiment were insufficiently prominent. The most commonly confused pairs of emotions, such as shame and sadness, surprise and fear, anger and disgust as well as confusions with neutral emotion were also given due attention.Conclusion.The work can contribute to psychological studies, clarifying emotion classification and gender aspect of emotionality, linguistic research, providing new evidence for prosodic and comparative language studies, and language technology, deepening the understanding of possible challenges for SER systems.

An investigation of the effect of race-based social categorization on adults' recognition of emotion.

Emotion recognition is important for social interaction and communication, yet previous research has identified a cross-cultural emotion recognition deficit: Recognition is less accurate for emotions expressed by individuals from a cultural group different than one’s own. The current study examined whether social categorization based on race, in the absence of cultural differences, influences emotion recognition in a diverse context. South Asian and White Canadians in the Greater Toronto Area completed an emotion recognition task that required them to identify the seven basic emotional expressions when posed by members of the same two groups, allowing us to tease apart the contributions of culture and social group membership. Contrary to our hypothesis, there was no mutual in-group advantage in emotion recognition: Participants were not more accurate at recognizing emotions posed by their respective racial in-groups. Both groups were more accurate at recognizing expressions when posed by South Asian faces, and White participants were more accurate overall compared to South Asian participants. These results suggest that in a diverse environment, categorization based on race alone does not lead to the creation of social out-groups in a way that negatively impacts emotion recognition.

Heterogeneity of long-history migration predicts emotion recognition accuracy.

Recent work (Rychlowska et al., 2015) demonstrated the power of a relatively new cultural dimension, historical heterogeneity, in predicting cultural differences in the endorsement of emotion expression norms. Historical heterogeneity describes the number of source countries that have contributed to a country's present-day population over the last 500 years. People in cultures originating from a large number of source countries may have historically benefited from greater and clearer emotional expressivity, because they lacked a common language and well-established social norms. We therefore hypothesized that in addition to endorsing more expressive display rules, individuals from heterogeneous cultures will also produce facial expressions that are easier to recognize by people from other cultures. By reanalyzing cross-cultural emotion recognition data from 92 papers and 82 cultures, we show that emotion expressions of people from heterogeneous cultures are more easily recognized by observers from other cultures than are the expressions produced in homogeneous cultures. Heterogeneity influences expression recognition rates alongside the individualism-collectivism of the perceivers' culture, as more individualistic cultures were more accurate in emotion judgments than collectivistic cultures. This work reveals the present-day behavioral consequences of long-term historical migration patterns and demonstrates the predictive power of historical heterogeneity. (PsycINFO Database Record

Considering cross-cultural context in the automatic recognition of emotions

Automatic recognition of emotions remains an ongoing challenge and much effort is being invested towards developing a system to solve this problem. Although several systems have been proposed, there is still none that considers the cultural context for emotion recognition. It remains unclear whether emotions are universal or culturally specific. A study on how culture influences the recognition of emotions is presented. For this purpose, a multicultural corpus for cross-cultural emotion analysis is constructed. Subjects from three different cultures—American, Asian and European—are recruited. The corpus is segmented and annotated. To avoid language artifacts, the emotion recognition model considers facial expressions, head movements, body motions and dimensional emotions. Three training and testing paradigms are carried out to compare cultural effects: intra-cultural, cross-cultural and multicultural emotion recognition. Intra-cultural and multicultural emotion recognition paradigms raised the best recognition results; cross-cultural emotion recognition rates were lower. These results suggest that emotion expression varies by culture, representing a hint of emotion specificity.

Cross-ethnic differences in perception of emotion in schizophrenia

Background The purpose of this study was to examine cross-ethnic differences in Perception of Emotion (POE) in schizophrenia. POE is an emerging construct in schizophrenia and involves the recognition and accurate identification of emotion in the facial and vocal expression of others. It has been implicated as relevant to instrumental functioning in schizophrenia, as well as a potential core deficit or marker for the disorder. Studies have shown the role of culture in shaping the expression and perception of emotion in non-clinical samples. It was hypothesized that ethnic minorities would have lower POE scores than Caucasians, and that the differences on POE would remain significant after controlling for neurocognition. Method Individuals, 131, diagnosed with schizophrenia or schizoaffective disorder participated in the study. There were 59 Euro-American Caucasian, 56 were African-American, and 16 were Latino. Neurocognition was measured as a standardized sum of five neuropsychological measures. Perception of Emotion was measured with facial and voice recognition tasks. Results Both Latinos and African-Americans scored lower on POE than Caucasians. The cross-ethnic differences on POE remained significant after controlling for neurocognition and overall symptom level. Post hoc analyses showed some support for the predictive validity of the POE measure across cultural contexts. Conclusions These results suggest that POE in schizophrenia is influenced by ethnicity, and that the ethno-cultural mechanisms influencing POE transcend the shared variation of POE and neurocognition. These results have implications for theories of cross-cultural emotion recognition, measurement bias in POE research, and for the place of culture in the study of POE in schizophrenia.

Cross-Cultural Emotion Recognition among Canadian Ethnic Groups

This study aims to investigate cultural differences in recognition accuracy as well as the in-group advantage hypothesis for emotion recognition among sub-Saharan African, Chinese, and French Canadian individuals living in Canada. The participants viewed expressions of happiness, anger, sadness, fear, disgust, and shame selected from the Montreal Set of Facial Displays of Emotion. These data did not support the in-group advantage hypothesis under the condition of stimulus equivalence. However, both encoder and decoder effects were found. Specifically, French Canadians were more accurate for the decoding of expressions of shame and sadness. Moreover, fear expressions were best recognized when shown by sub-Saharan Africans, suggesting an effect of salience of expressive cues due to morphological features of the face.