Path Models of Vocal Emotion Communication

This paper argues that emotional inferences about characters in a text are not as specific as previously assumed (DeVega, Diaz, & Leon, 1997; DeVega, Leon, & Diaz 1996; Gernsbacher, Goldsmith, & Robertson, 1992; Gernsbacher, Hollada, & Robertson, 1998; Gernsbacher & Robertson, 1992). The emotional information inferred by readers does not differentiate between emotions that are similar, though not identical. In both Experiments 1 and 2, participants read the stories used by Gernsbacher et al. (1992). Results from Experiment 1 (off-line) show that participants judged several emotions consistent with the same story. In Experiment 2 (on-line), participants took longer to read target sentences containing emotions mismatching the stories, but there was no difference between target sentences containing different matching emotions as determined by Experiment 1. Results from Experiments 1 and 2 suggest that the emotional information readers infer from the stories is too broad to determine a specific emotion. The results are consistent with the idea that a general emotional response is evoked, which is compatible with one or more specific emotions.

This chapter explores how leaders influence their people and the climate of the organization by the emotions they show and cause in others by their leadership. It examines the dynamics of this emotional influence, illuminating how leaders’ expressions influence climate and performance through (1) emotional contagion—whereby members mimic the emotion shown—and (2) emotional inference—whereby members draw conclusions based on leaders’ emotional expressions, with power magnifying the effect of emotional expressions. The chapter underscores how positive emotions have benefits but must be shown with authenticity and vulnerability to avoid the pitfalls of toxic positivity. Finally, the chapter explores how leadership behaviors evoke emotions. The chapter explains how to navigate “necessary evils” such as layoffs and delivering critical feedback, providing specific strategies for employing compassionate detachment during these difficult conversations.

The ability to recognize emotion is important to wellbeing and building relationships with others, making this skill important in adolescence. Research investigating adolescents’ ability to recognize facial and vocal emotion expressions has reported differing conclusions about the pattern of emotion recognition across this developmental period. This systematic review aimed to clarify the pattern of recognition for facial and vocal emotion expressions, and the relationship of performance to different task and emotion expression characteristics. A comprehensive and systematic search of the literature was conducted using six databases. To be eligible for inclusion, studies had to report data for adolescents between 11 and 18 years of age and measure accuracy of the recognition of emotion cues in either the face or voice. A total of 2333 studies were identified and 47 met inclusion criteria. The majority of studies focused on facial emotion recognition. Overall, early, mid-, and late-adolescents showed a similar pattern of recognition for both facial and vocal emotion expressions with the exception of Sad facial expressions. Sex of the participant also had minimal impact on the overall recognition of different emotions. However, analysis showed considerable variability according to task and emotion expression characteristics. Future research needs to increase focus on recognition of complex emotions, and low-intensity emotion expressions as well as the influence of the inclusion of Neutral as a response option.

Results from recent event-related brain potential (ERP) studies investigating brain processes involved in the detection and analysis of emotional facial expression are reviewed. In all experiments, emotional faces were found to trigger an increased ERP positivity relative to neutral faces. The onset of this emotional expression effect was remarkably early, ranging from 120 to 180ms post-stimulus in different experiments where faces were either presented at fixation or laterally, and with or without non-face distractor stimuli. While broadly distributed positive deflections beyond 250ms post-stimulus have been found in previous studies for non-face stimuli, the early frontocentrally distributed phase of this emotional positivity is most likely face-specific. Similar emotional expression effects were found for six basic emotions, suggesting that these effects are not primarily generated within neural structures specialised for the automatic detection of specific emotions. Expression effects were eliminated when attention was directed away from the location of peripherally presented emotional faces, indicating that they are not linked to pre-attentive emotional processing. When foveal faces were unattended, expression effects were attenuated, but not completely eliminated. It is suggested that these ERP correlates of emotional face processing reflect activity within a neocortical system where representations of emotional content are generated in a task-dependent fashion for the adaptive intentional control of behaviour. Given the early onset of the emotion-specific effects reviewed here, it is likely that this system is activated in parallel with the ongoing evaluation of emotional content in the amygdala and related subcortical brain circuits.

The study of human vocal communication has been conducted primarily in Western, educated, industrialized, rich, democratic (WEIRD) societies. Recently, cross-cultural investigations in several domains of voice research have been expanding into more diverse populations. Theoretically, it is important to understand how universals and cultural variations interact in vocal production and perception, but cross-cultural voice research presents many methodological challenges. Experimental methods typically used in WEIRD societies are often not possible to implement in many populations such as rural, small-scale societies. Moreover, theoretical and methodological issues are often unnecessarily intertwined. Here, I focus on three areas of cross-cultural voice modulation research: (i) vocal signalling of formidability and dominance, (ii) vocal emotions, and (iii) production and perception of infant-directed speech. Research in these specific areas illustrates challenges that apply more generally across the human behavioural sciences but also reveals promise as we develop our understanding of the evolution of human communication.This article is part of the theme issue ‘Voice modulation: from origin and mechanism to social impact (Part II)’.

Lexical tones and emotions are conveyed by a similar set of acoustic parameters; therefore, listeners of tonal languages face the challenge of processing lexical tones and emotions in the acoustic signal concurrently. This study examined how emotions affect the acoustics and perception of Mandarin tones. In Experiment 1, Mandarin tones were produced by professional actors with angry, fear, happy, sad, and neutral tones of voice. Acoustic analyses on mean F0, F0 range, mean amplitude, and duration were conducted on syllables excised from a carrier phrase. The results showed that emotions affect Mandarin tone acoustics to different degrees depending on specific Mandarin tones and specific emotions. In Experiment 2, selected syllables from Experiment 1 were presented in isolation or in context. Listeners were asked to identify the Mandarin tones and emotions of the syllables. The results showed that emotions affect Mandarin tone identification to a greater extent than Mandarin tones affect emotion recognition. Both Mandarin tones and emotions were identified more accurately in syllables presented with the carrier phrase, but the carrier phrase affected Mandarin tone identification and emotion recognition to different degrees. These findings suggest that lexical tones and emotions interact in complex but systematic ways.

Specificity of childhood maltreatment and emotion deficit in nonsuicidal self-injury in an inpatient sample of youth

As the field of school psychology faces critical shortages, investigations of work factors affecting job satisfaction and burnout are of increasing importance. One such factor is emotional labor, which is defined as the work of managing one’s emotions and emotional expressions so as to align to the expectations of the job or profession. In this study, practitioners (N = 192) were surveyed regarding their work experiences, recognition of display rules (standards that guide employees’ emotional expression), surface acting (the form of emotional labor in which employees manage their external emotional expression), job satisfaction, and burnout (consisting of emotional exhaustion, depersonalization, and personal accomplishment). Multiple regression analyses showed that display rule recognition was positively related to surface acting, and surface acting was positively related to emotional exhaustion and depersonalization while negatively related to job satisfaction and personal accomplishment. Results suggest that emotional labor may be an important aspect of the work of school psychologists—impacting both job satisfaction and burnout. Limitations and implications for research and practice are discussed.

T Human-Computer Interaction recently received an increased attention. This is not just a matter of making the operation of technical systems as simple as pissuble, but also to enable a possibly natural interaction. In this context, especially the speech-based operation gained an increased attention. For example, modern smart phones and televisions offer a robust voice control, which is attributed to various technical improvements in recent years. Nevertheless, voice control still seems artificial. Only self-contained dialogues with short statements can be managed. Furthermore, just the content of speech is evaluated. The way in which something is said remains unconsidered, although it is known from human communication, that the transmitted emotion is important in order to communicate successfully. A relatively new branch of research, the “Affective Computing”, has, amongst other objectives, the aim to develop technical systems that recognise and interpret emotions and respond to them appropriately. In this case, speech-based automatic emotion recognition has a major role. For emotion recognition, it is important to know how emotions can be presented and how they are expressed. For this purpose, it is helpful to rely on empirical evidences of the psychology of emotions. Unfortunately, there is no uniform representation of emotions. Also the definition of appropriate emotion-distinctive acoustic features is rather descriptive in psychology. Therefore, the automatic detection of emotions is based on proven methods of automatic speech recognition, which have also been shown as appropriate for emotion recognition. Automatic emotion recognition is, as speech recognition, a branch of pattern recognition. Contrary to emotion psychology, it is data-driven, that means insights are gathered from sampled data. For emotion recognition the phases “annotation”, “modelling” and “recognition” are distinguishable. The annotation categorises speech data according to predefined emotion-terms. Modelling generates recognisers to categorise data automatically. Recognition performs a previously unknown allocation of data to emotional classes. In the beginning, automatic emotion recognition was usually based – due to the lack of suitable data sets – on acted and very expressive emotional expressions. In this case, based on features and detection methods known from speech recognition, very good recognition results of over 80% in distinguishing of up to seven emotions could be achieved. However, for human-machine interaction these recognisers were unsuitable because in this case emotions are not that expressive. Therefore, in collaboration with

Introduction: Parkinson’s disease (PD) commonly causes speech impairments, including difficulties in expressing emotions through voice. Method: The objective of this study was to investigate gendered vocal expressions of fear, anger, sadness, and happiness for mild to moderate PD. Prosodic features (related to fundamental frequency (F0), intensity (I), speech rate, articulation rate, and number and duration of pauses) and acoustic correlates of voice quality (CPPS, jitter, shimmer, and HNR) were collected from 14 patients with PD (mean age = 69.93; SD = 7.12; 8 males, 6 females) and 13 healthy controls (HC) (mean age = 68.13; SD = 8.27; 5 males, 8 females) matched for age, sex, and years of education. The utterances were extracted from four emotional and one neutral text. The neutral utterance and the emotional utterances were compared. Intra-sex comparison (female with PD vs. female HC and male with PD vs. male HC) and inter-sex comparison (female vs. male both for patients with PD and for HC), were performed with the Mann–Whitney test. A Mann–Whitney test was also used to compare the different emotional conditions, considering sex and PD diagnosis as well. Results: No significant intra-sex differences were found for the neutral speech, but inter-sex differences emerged. Regarding emotional speech, females with PD featured lower MaxF0 than female HCs for happiness and higher intensity variability (SD I) for sadness. Utterances by females with PD had lower CPPS than utterances by HCs for anger and fear. Utterances by males with PD had lower minimum intensity (MinI) than utterances by male HCs when expressing fear. Conclusions: Emotional vocal expression in individuals with PD was found to be impaired and showed sex differences. These findings have the potential to significantly impact the quality of life of PD patients.

The purpose of the article. Modern special psychology and pedagogy in Ukraine are aimed not only at preserving the acquired experience, but at transforming the acquired knowledge in order to create conditions for the realization of personality with intellectual disabilities today. The research in technologies of correction-compensatory work with persons with peculiarities of psychophysical development is becoming increasingly relevant. The relevance of the study of aspects of emotional intelligence of persons with intellectual disabilities is conditioned by the solution of the urgent need to develop effective technologies for the development of personality with special educational needs at different age stages of its formation. Methods. The complexity, lack of research and ambiguity of the phenomenon of the concept of «emotional intelligence» in adolescents with intellectual disabilities led to the choice of research method – modeling. Specific research methods, including psychodiagnostic methods, have also been used to solve problems and ensure the validity of propositions and conclusions. One of the components of emotional intelligence of adolescents with intellectual disabilities – emotional – was studied. The method «Testing the ability to recognize emotions based on pantomime masks of the human face» (adaptation of M. Lebedeva), the method «Determination of the ability to differentiate emotional states» (according by V. Matveev), conversations, observations were used in the article. The results of the research. In the course of psychodiagnostic, the attention was focused on determining the state of formation of the emotional component and the levels of representation of its indicators. These are important qualitative characteristics such as recognition, differentiation and verbalization of emotions. It was found that the emotional component in adolescents with mental disabilities is characterized by an average level of formation. This indicates that adolescents are capable of partial understanding, analysis, and comparison of emotional states with their own experience (3–4 states); partial identifying, measuring, verbalization of emotions (4–5 basic emotions) and their psycholinguistic description. Conclusions. In the course of the research an attempt was made to establish the specifics of the functioning of emotional intelligence of a teenager with mental development disorders through the selection of key components in order to further influence the formation of the personality of the studied category of adolescents. In particular, one of the components of the presented model of emotional intelligence was studied and the results of the study of one of the components were considered, in particular, the emotional component in the structure of emotional intelligence of adolescents with intellectual disabilities. Identified components, their research (emotional and other components), establishing indicators of their formation and establishing levels of emotional intelligence are the basis for further development of effective psychological support for adolescents with intellectual disabilities.

Non-motor impairment such as emotion recognition deficit in both facial and vocal expressions has been previously reported in Parkinson's disease (PD). We investigated whether the decoding of emotional prosody is impaired in PD and whether this deficit is related to striatal damage. Fifteen PD patients and 15 healthy controls (HCs) were requested to listen to six audio tracks and to recognize the emotions expressed by a professional actor while reading a meaning-neutral sentence. All subjects also received a structural MRI examination. Volumetric measurements were extracted for the striatum, a key region involved in emotional processing and typically impaired in PD. Decoding sadness conveyed by voice was impaired in PD compared with HC and was related to the volume of the dorsal striatum bilaterally. The dorsal striatum is involved in the decoding of vocal negative emotions in PD.

Emotional inferences from speech require the integration of verbal and vocal emotional expressions. We asked whether this integration is comparable when listeners are exposed to their native language and when they listen to a language learned later in life. To this end, we presented native and non-native listeners with positive, neutral and negative words that were spoken with a happy, neutral or sad tone of voice. In two separate tasks, participants judged word valence and ignored tone of voice or judged emotional tone of voice and ignored word valence. While native listeners outperformed non-native listeners in the word valence task, performance was comparable in the voice task. More importantly, both native and non-native listeners responded faster and more accurately when verbal and vocal emotional expressions were congruent as compared to when they were incongruent. Given that the size of the latter effect did not differ as a function of language proficiency, one can conclude that the integration of verbal and vocal emotional expressions occurs as readily in one's second language as it does in one's native language.

In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors. Keywords: classification, COVID-19, emotion, emotion analysis, Naïve Bayes, Pandemic, Random Forest, SVM.

Parental support is a predictor of children's career interest and aspirations. However, mother and father support affects youth career choices differently. To understand how perceived mothers' and fathers' support affect career interest in computer science (CS), we developed two path models using both mother and father support gains to predict youths' interest in CS. We hypothesized that perceived father's and mother's support would relate to youths' interest in CS via youths' perception of CS utility value as a mediator. We found that both mother and father support leads to interest in CS. However, father support was found to affect CS interest via the mediator utility-value beliefs. To provide explanations for these differences we used student interview data to explore how participants in our study perceived parental support.