Tone Of Voice Research Articles

“Irony is easy to understand ”: The role of emoji in irony detection

Abstract In face-to-face conversations, interlocutors might recognize the ironic intent of a speaker relying on the incongruity of the comment relative to a situation, and on irony markers such as the ironic tone of voice and specific facial expressions. In instant messaging, acoustical and visual cues are typically absent, and the context is not always shared. We investigated the role of emoji as cues to detect irony, hypothesizing that they might play the role of the conversational context. We administered to 156 Italian adults a questionnaire, presenting them with WhatsApp messages followed by an emoji, which was congruent or incongruent with the (non-)evaluative positive or negative comment, and found that evaluative incongruent items were rated as more ironic, and that incongruent positive messages were more easily recognized as ironic (criticisms) compared to incongruent negative messages (ironic compliments), in line with the asymmetry of affect hypothesis.

Do Robots Have Sex? A Prolegomenon

Research in Human–Robot Interaction (HRI) suggests that people attribute gender to (some) robots. In this paper we outline a program of research on the gendering of robots and on the ethical issues raised by such gendering. Understanding which robots are gendered, when, and why, will require careful research in HRI, drawing on anthropology and social psychology, informed by state-of-the-art research in gender studies and critical theory. Design features of robots that might influence the attribution of gender include: appearance; tone of voice; speech repertoire; range and style of movement; behaviour; and, intended function. Robots may be gendered differently depending on: the age, class, sex, ethnicity, and sexuality of the person doing the attributing; local cultural histories; social cues from the designers, the physical and institutional environment, and other users; and the role of the robot. An adequate account of the gender of robots will also need to pay attention to the limits of a sex/gender distinction, which has historically been maintained by reference to a “sex” located in a biological body, when it comes to theorising the gender of robots. We argue that, on some accounts of what it is to be sexed, robots might “have” sex: they might be male and female in just the same way as (most) human beings are. Addressing the ethical issues raised by the gendering of robots will require further progress in “robot media ethics”, as well as an account of the responsibilities of both designers and users in a broader social context.

The sound of education: Exploring Filipino students' perceptions of teachers' vocal quality in the classroom

Every teacher needs to develop their vocal skills through continual practice and voice quality improvement. Students will draw judgments and assumptions about the teacher's attitude toward them, as well as the knowledge and concepts that he or she is imparting, based on the tones of the teacher's voice. The purpose of this study was to explore students’ perception on teachers’ vocal quality in a classroom setting. With a qualitative research design, the current study employed an interpretative phenomenological analysis approach. The participants of the study are composed of 26 students (9 males) (17 females) under the College of Business and Entrepreneurship. The collection of data was done through a face-to-face, semi-structured interview with audio recording. With the help of thematic analysis, the researchers come up with these themes: (1) the instructional tone or manner and emotional expression of the teachers; (2) teachers’ emphasis and motivation on teaching; (3) engagement and voice characteristics of teachers; (4) teachers’ effectiveness and students’ engagement in the classroom; (5) challenges faced by students; (6) comprehensibility and academic performance of students; and (7) learning and knowledge acquisition of students. The findings revealed that the vocal quality of teachers have an impact on how engaged their students are in the learning process, which in turn helps them achieve academically.

Systematic manipulation of experimenters' non-verbal behaviors for the investigation of pain reports and placebo effects.

Non-verbal behaviors (NBs) of caregivers affect pain reports and placebo effects. However, little experimental research has systematically examined the caregivers' NBs. This study protocol and preparatory study report a systematic manipulation of experimenters' NBs to investigate pain report and placebo effects. We propose an experiment in which videotaped experimenters (VEs) conduct a pain stimulation and a placebo treatment study. The VEs express one positively enhanced NB and keep the other NBs neutral. Participants will be randomized to either the positive facial expressions (+FE), tone of voice (+TV), body movement (+BM), or neutral NBs (i.e., neutral condition; NC) of the VEs. As a preparatory study for proof of concept, two groups of NB coders from Norway and the USA separately rated the degree of NBs (eye contact, body postures and movements, and tone of voice), and impressions of dominance and being in charge, positivity, and expressivity from each NB video. The NB videos had construct validity and reliability. The +BM and +FE were rated as more dominant and in charge than the +TV and the NC. The +FE and +BM were rated as the most positive and expressive NBs, respectively. +FE will have the largest placebo effects on pain and stress levels. However, transmitting the NBs to patients by VEs is challenging. Moreover, controlling for the effects of research assistants present in the testing room is challenging. We propose that caregivers' NBs affect pain reports and placebo effects. Moreover, different NBs elicit different impressions, and a better understanding of the role of caregiver NBs requires more rigorous investigations. Lastly, aiming to investigate the caregiver NBs, the varying degrees of micro-NBs and their effects on the formation of impressions should be considered.

How neurotypical listeners recognize emotions expressed through vocal cues by speakers with high-functioning autism.

We conducted an investigation to explore how neurotypical (NT) listeners perceive the emotional tone of voice in sentences spoken by individuals with high-functioning autism spectrum disorders (ASD) and NT speakers. The investigation included both male and female speakers from both groups. In Study 1, NT listeners were asked to identify the emotional prosody (anger, fear, happiness, surprise or neutral) conveyed by the speakers. Results revealed that emotional expressions produced by male ASD speakers were generally less accurately recognized compared to male NT speakers. In contrast, emotions expressed by female ASD speakers were more accurately categorized compared to female NT speakers, except when expressing fear. This suggests that female ASD speakers may not express emotional prosody in the same way as their male counterparts. In Study 2, a subset of produced materials was rated for valence, voice modulation, and voice control to supplement Study 1 results: Female ASD speakers sounded less negative when expressing fear compared to female NT speakers. Male ASD speakers were perceived as less positive than NT speakers when expressing happiness. Voice modulation also differed between groups, showing a tendency for ASD speakers to follow different display rules for both positive emotions (happiness and surprise) tested. Finally, male ASD speakers were rated to use voice cues less appropriately compared to NT male speakers, an effect less pronounced for female ASD speakers. Together, the results imply that difficulties in social interactions among individuals with high-functioning ASD could be due to non-prototypical voice use of male ASD speakers and emphasize that female individuals do not show the same effects.

Development of an Industrial Safety System Based on Voice Assistant

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users’ cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based on a voice assistant, Alexa, which promotes more natural, intuitive, direct, and understandable communication. The purpose of this work is the development of an industrial safety system for a controlled electric motor based on Alexa voice assistant, which allows the monitoring of its operating parameters, such as phase current, housing temperature, and rotor vibration, as well as making it possible to control ignition and shut down and change the rotation of the motor with a prior password, as a safety measure. Commercial smart devices and Arduino-compatible modules were used to achieve this, providing them with the Internet of Things (IoT) feature. In addition, several software platforms, such as Blynk, Tuya Smart, Node Red, and Voiceflow, are used to perform data transmission, device management, and programming of the Alexa skill, oriented to the execution of the security and run system. This shows the potential capacity of voice assistants in the industry to deliver information more naturally to humans and obtain optimal notifications. However, problems were evidenced, such as the influence of noise in the environment when communicating with the assistant, the vocalization of words, low voice tones, and accents typical of the language, that will increase the security level of the system and prevent potential identity theft.

Separated and reunified: An apparent time investigation of the voice quality differences between Hong Kong Cantonese and Guangzhou Cantonese.

Hong Kong Cantonese (HKC) and Guangzhou Cantonese (GZC) are two major accents of Cantonese spoken in two geographically non-contiguous cities in Southern China. Previous studies were unable to identify the phonetic features that discern the two accents since they share the same phonological system. This study attempted to solve the puzzle by investigating the voice quality differences between the two accents through acoustic analysis on the speech output of 191 talkers in three age groups ranging from 18 to 65 years old. Among the various spectral and noise measurements of voice quality, we found that Cepstral Peak Prominence (CPP) was the best acoustic measure to discern the two accents. Based on the CPP measure, GZC had overall increased noise than HKC. Covariation of voice quality and tones was studied. The greatest CPP differences between the two accents were found in the two extreme tones: the high-level and the extra-low-level tones. Furthermore, creaky voice was found mainly tied to the extra-low-level tone in both accents. However, HKC exhibited higher frequency of creaky voice than GZC. The creaky voice in GZC was characterized by increased noise and increased tension, compared to those of HKC. Finally, age was found to be a mediating factor in the voice quality of the two accents. Adopting the Apparent Time Framework, voice quality in the two cities has undergone changes over time. The voice quality of the young generations of the two accents have become merged among the three low tones. Furthermore, the prevalence of creaky voice was increasing across age groups in both accents, and it increased at a faster rate in HKC than GZC.

Writing Orality: Australian Aboriginal Voices in Alexis Wright’s Carpentaria

ABSTRACT Australian Aboriginal stories have thrived for thousands of years through oral tradition and Aboriginal author Alexis Wright invokes this tradition in the construction of her novel Carpentaria. This article investigates the orality of Carpentaria, which stages “oral” narrators who speak differently to Aboriginal and non-Aboriginal readerships. First, using Bakhtin’s notion of “speech genre”, the article explores why Wright creates these two narrative layers. Second, it investigates the language use and tone of voice in the framing narrative that addresses non-Indigenous readers. Third, it looks closely at Wright’s linguistic experimentation in the embedded narrative, creating multiple oral effects through language and mobilising the storytelling dynamics of performance, spontaneity, rhythms and mnemonics. Finally, it discusses how her creative use of orality plays off and with its Western literary conceptions and enacts cross-cultural communication between the two readerships.

Protecting organizational reputation during a para-crisis: The effectiveness of conversational human voice on social media and the roles of construal level, social presence, and organizational listening

This study examines the efficacy of conversational human voice (CHV) in protecting organizational reputation during a para-crisis on social media. Using a 2 × 2 experimental design, the study manipulates tone of voice (CHV vs. organizational voice) and construal level (concrete vs. abstract) to identify the underlying mechanisms and boundary conditions. The findings demonstrate that CHV surpasses organizational voice in bolstering social presence and perceived organizational listening, ultimately fostering a positive impact on organizational reputation amidst para-crises on social media. Nevertheless, the effectiveness of CHV is contingent upon the construal of crisis messages. Specifically, CHV becomes particularly effective in reputation management solely when crisis messages are conveyed in a concrete manner. The study highlights the importance of incorporating CHV into crisis communication strategies and offers practical guidance for organizations to communicate effectively on social media during a crisis.

A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods

Affective computing is one of the most important research fields in modern human–computer interaction (HCI). The goal of affective computing is to study and develop the theories, methods, and systems that can recognize, explain, process, and simulate human emotions. As a branch of affective computing, emotion recognition aims to enlighten the machine/computer automatically analyzing human emotions, which has received increasing attention from researchers in various fields. Human beings generally observe and understand the emotional states of one person by integrating the perceived information from his/her facial expressions, voice tone, speech content, behavior, or physiological features. To imitate the emotion observation manner of humans, researchers have been devoted to constructing multimodal emotion recognition models by fusing information from two or more modalities. In this paper, we provide a comprehensive review of multimodal emotion recognition from the perspectives of multimodal datasets, data preprocessing, unimodal feature extraction, and multimodal information fusion methods in recent decades. Furthermore, challenges and future research directions of the topic are specified and discussed. The main motivations of this review are to conclude the recent emergence of abundant works on multimodal emotion recognition and to provide potential guidance to researchers in the related field for understanding the pipeline and mainstream approaches to multimodal emotion recognition.

Отражение профессиональных установок языковой личности в образе пожарного в сказке И. В. Зартайской «Весёлый пожарный»

The paper considers the professional attitudes of a firefighter’s linguistic personality using the example of the image of a firefighter in the children’s tale “The Cheerful Firefighter” by I. V. Zartayskaya. The scientific novelty of the study lies in the fact that professional attitudes in the structure of a firefighter’s linguistic personality have not been examined before: the paper is the first to discover that the linguistic model of a firefighter realises itself in stereotypes associated with the notions of “seriousness” and “bravery” and to outline the features of a professional’s speech activity corresponding to these categories. At the same time, a literary text was used for the study, while the linguistic personality of a firefighter was investigated previously only within the framework of business communication. Hence, the paper aims to identify the professional attitudes of a firefighter in speech interaction. As a result, the examination of a firefighter’s image in I. V. Zartayskaya’s children’s tale “The Cheerful Firefighter” led to the discovery of professional attitudes associated with the notions of “seriousness” and “bravery”, which exclude speech behaviour in the form of teasing, performing humourous songs, joking, smiling, etc., as well as imply the use of speech means and techniques that determine the successful resolution of an extreme situation, e.g., empathic listening, contact-establishing dialogue, calm tone of voice, etc. Thus, the speech characteristics of the protagonist presented in the work reflect the professional attitudes of a firefighter’s linguistic personality correlating with the categories of “seriousness” (the speech behaviour of a “person in uniform” does not allow for the expression of entertainment, comedy and demonstration of wit, since the SERIOUSNESS concept denotes a moral criterion the measure of which is a sense of proportion and the absence of any playful forms of speech behaviour) and “bravery” (the speech activity of a firefighter is determined by the ability to correctly interpret information and resolve conflicts in an emergency situation, use speech support tactics, as well as voice skills and clear sound pronunciation).

Neuro-Linguistic Programming in Controlling the Subcon scious Mind to Acquire Foreign Languages

Neuro-linguistic programming (NLP) is a powerful tool that has gained popularity in recent years for its effectiveness in reprogramming the subconscious mind and acquiring foreign languages. By understanding the principles of NLP and applying them to language learning, individuals can unlock their linguistic potential and improve their proficiency in a foreign language. This essay will explore how neuro-linguistic programming can be utilized to harness the power of the subconscious mind and facilitate language acquisition.One of the fundamental concepts in neuro-linguistic programming is the idea that the subconscious mind is a powerful force that shapes our thoughts, behaviors, and beliefs. Through various techniques, such as visualization and affirmations, individuals can influence and program their subconscious mind to acquire foreign languages more effectively. By consistently exposing oneself to the target language through listening, reading, and speaking, the subconscious mind begins to assimilate the linguistic patterns and structures, ultimately leading to improved language proficiency. NLP helps individuals tap into the vast potential of their subconscious mind and utilize it as a tool for language acquisition.Another crucial aspect of neuro-linguistic programming is the recognition of different learning styles and preferences. People have unique ways of processing and assimilating information, and NLP acknowledges this diversity. By identifying one's preferred learning style, individuals can tailor their language learning approach accordingly. For instance, visual learners may benefit from using mind maps or flashcards to associate words with images, while auditory learners may find listening to native speakers or audio materials helpful. NLP provides a framework for individuals to discover their learning styles and leverage them to accelerate language acquisition.In addition to understanding learning styles, neuro-linguistic programming also emphasizes the importance of effective communication. Language learning is not only about vocabulary and grammar but also about developing excellent communication skills in the target language. NLP techniques can improve individuals' ability to understand and express themselves in a foreign language, including body language, tone of voice, and non-verbal cues. By practicing effective communication strategies, such as mirroring or pacing, language learners can establish rapport with native speakers and enhance their overall fluency.

Emotional Ambience in Interaction Rituals: A Conceptional Completion to Emotional Energy

This article aims to elaborate on Collins’ theory of Interaction Ritual Chains by proposing the concept of emotional ambience as a complement to emotional energy. Interaction ritual chains describe how collective actions and shared cognitive and affective orientations within a group contribute to feelings of unity and reverence towards the group’s symbols. Successful interaction rituals generate emotional energy (EE), leading to increased self assurance, enthusiasm, and initiative. Conversely, unsuccessful rituals diminish EE. The concept of EE pertains to the long-term impact of interaction rituals on individuals beyond immediate contexts. To capture emotions created and diffused in social settings, the term emotional ambience is suggested. Emotional ambience focuses on the collective emotional process in an interaction situation, enhancing our understanding of how common sentiments are cultivated among actors during interaction rituals. To facilitate the analysis of emotional ambience, a three-dimensional model is proposed, considering the valence, arousal, and strength of collective emotions. Methodologically, the study focuses on the emotional coordination of various communication elements, such as gestures, facial expressions, tone of voice, and rhythm of speech. Understanding the separation of emotional energy and emotional ambience is crucial, as even the mutual sharing of unpleasant emotions can generate emotional energy and strengthen social bonds. The reciprocal relationship between emotional energy and emotional ambience highlights how individuals’ emotional energy influences the emotional ambience of interactional situations.

Establishing an optimal working relationship with patients with an antisocial personality disorder. Aspects and processes in the therapeutic alliance.

Developing good interpersonal relationships is one of the main impediments for people with an antisocial personality disorder (ASPD). However, in treatment of psychiatric disorders, establishing a strong therapeutic alliance (TA) is important for effective treatment. Nevertheless, there is little knowledge on how to establish this TA with this challenging patient group. This study investigates which factors are important in TA development. For this study, a qualitative research methodology is applied. In-depth interviews with therapists experienced in treating ASPD were conducted and analysed through thematic analysis. The analysis revealed six themes important in alliance formation: the patient's needs, regulating interpersonal dynamics, connective attitude, connective skills, treatment process and treatment goals. Each theme is defined including aspects of the recommended therapeutic attitude and required skills for therapists working with patients with ASPD. This study determined that, for therapists working with patients with ASPD, several key factors are essential in establishing a strong TA. These factors include the ability to be firm, authentic, non-judgmental and genuinely involved. An attentive presence is crucial, in which the therapist takes initiative in establishing contact and makes the patient feel that he is truly seen as an autonomous and equal person. In doing so, the therapist needs to provide clarity and structure while remaining perceptive to boundary violations. The therapist must be able to set limits using a clear yet kind tone of voice. Furthermore, it was notable that an intensive appeal is made to the therapist's reflective capacity in these treatments.

YOUTUBE VIDEO RECOMMENDATION BASED ON FACIAL EXPRESSION

Human faces play a significant role in determining a person's mood. Emotion plays an important role in various fields, including biomedical engineering, brain science, neuroscience, psychological wellness, and mental health. Emotions are not only used for determination of the state of the human brain but too utilized as a proposal framework to help people in finding things that coordinate their needs and inclinations. This motivated us to create a framework that can viably and proficiently recognize feelings from the facial expressions of the user and recommend the YouTube videos based on that emotion. Emotions are bought by the neurophysiological changes that are associated with thoughts, feelings and behavioral changes in a person. Every emotion needs to be treated in a right way and research suggests that watching YouTube videos influences your mood, and in our experiment, we try to influence it in a good way. We can detect changes in emotion by observing facial expressions, body language, and tone of voice. There are many indicators of a person's mood, but facial expressions are one of the most important. In our experiment, we used the Haar Cascade Classifier to detect the face and a convolution neural network to analyze the facial expression. Performance of our proposed model is 95.66% for face detection and 61.88% for emotion detection. Key Words: Emotion Detection, FER-2013, HAAR Cascade, YouTube Recommendation, CNN.

Teachers’ Non-Verbal Communication in Teaching English to Young Learners

The ability to communicate nonverbally is a skill that prospective teachers must master. This current study was to explore the the usage of non-verbal communication by teachers in the highly popular YouTube video titled "How to teach Kids | from a Prague kindergarten, part 2 | English for Children. Based on the non-verbal communication classification by Marcinowicz et al. (2010), which includes Eye contact, Facial expressions, Touch, Interpersonal distance, Gesture, Posture, Tone of voice, and Dress, this study adopts a qualitative approach to analyze the teacher's communication strategies. The results showed that, overall, using nonverbal communication strategies such as eye contact, facial expressions, gestures, tone of voice, and dress by the teachers in the video can enhance the quality of learning and build a good relationship between teachers and students. These strategies can create a fun learning atmosphere, motivate students, and strengthen their understanding of the learning material.

Enhancing Human-Machine Interaction: Real-Time Emotion Recognition through Speech Analysis

Humans, as intricate beings driven by a multitude of emotions, possess a remarkable ability to decipher and respond to socio-affective cues. However, many individuals and machines struggle to interpret such nuanced signals, including variations in tone of voice. This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations. In particular, the authors propose a real-time processing method that captures and evaluates emotions in speech, utilizing a terminal device like the Raspberry Pi computer. Furthermore, the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology, which involves analyzing audio files from renowned emotional speech databases. To aid incomprehension, the authors present visualizations of these audio files in situ, employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib. The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions. Notably, the classification accuracies achieved are 70% and 77%, respectively, demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server. The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker's emotional state in less than one second on a Raspberry Pi. These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains.

Feelings on feedback: Children’s emotional responses during mathematics problem solving

Theories of learning emphasize the importance of both the cognitive and affective state of the learner. The current study focused on children’s affective reactions to corrective feedback during mathematics problem solving. Eighty-seven elementary school children (M age = 7.6 years, 41% female, 68% White) solved mathematical equivalence problems during an online video call and received trial-by-trial feedback on their answers. Trained researchers used children’s facial expressions, tone of voice, and verbal statements to quantify their positive and negative affect on each trial. Overall, children tended to express more positive affect than negative affect. However, negative affect was more prominent when the child was incorrect and received negative feedback, and higher negative affect was associated with lower accuracy and lower persistence on the task. These results provide novel empirical evidence for the role of emotions during children’s STEM learning in a non-evaluative context.

Multimodal Prompt Learning in Emotion Recognition Using Context and Audio Information

Prompt learning has improved the performance of language models by reducing the gap in language model training methods of pre-training and downstream tasks. However, extending prompt learning in language models pre-trained with unimodal data to multimodal sources is difficult as it requires additional deep-learning layers that cannot be attached. In the natural-language emotion-recognition task, improved emotional classification can be expected when using audio and text to train a model rather than only natural-language text. Audio information, such as voice pitch, tone, and intonation, can give more information that is unavailable in text to predict emotions more effectively. Thus, using both audio and text can enable better emotion prediction in speech emotion-recognition models compared to semantic information alone. In this paper, in contrast to existing studies that use multimodal data with an additional layer, we propose a method for improving the performance of speech emotion recognition using multimodal prompt learning with text-based pre-trained models. The proposed method is using text and audio information in prompt learning by employing a language model pre-trained on natural-language text. In addition, we propose a method to improve the emotion-recognition performance of the current utterance using the emotion and contextual information of the previous utterances for prompt learning in speech emotion-recognition tasks. The performance of the proposed method was evaluated using the English multimodal dataset MELD and the Korean multimodal dataset KEMDy20. Experiments using both the proposed methods obtained an accuracy of 87.49%, F1 score of 44.16, and weighted F1 score of 86.28.

TEACHING SIMULTANEOUS TRANSLATION IN SOCIAL NETWORKS TO FUTURE INTERPRETERS

The article is devoted to teaching the main features of simultaneous translation in social networks, taking into account the specifics of this genre of communication. The relevance of the issue is that now more and more people are using social networks as a means to promote their business, and thus many experts and brands need translators, and especially simultaneous ones, who could help adequately represent products and services on the international market. The purpose of the article is to analyze the main challenges that translators face when working with text messages, comments and posts in social networks, namely the speed of reaction, the importance of context and non-standard formats. In addition, the peculiarities of translation in various social networks, such as Twitter, Facebook, Instagram, LinkedIn, and their influence on the process of simultaneous translation are considered. In the course of the research, such methods as observation, synthesis and analysis of information were carried out. The article presents an analysis of the main features of simultaneous translation, namely speed, brevity, informality, a large amount of visual content, emotional aspect, tone of voice and their structural-grammatical and lexical functions. The article contains recommendations for translators on effective and accurate simultaneous translation in social networks. Informational and explanatory functions, to which the whole article is devoted, are only its secondary purpose. The following main methods of simultaneous translation were also highlighted: functional substitution, transcription, transliteration, descriptive translation (explication), reduction, expansion and borrowing. In addition to this, we identified the main approaches used in simultaneous interpreting, namely targeting real-time, blogger, audience, social network and content. In addition, key aspects have found their way into translator training profiles and quality standards for translation services. Central to the acquisition of translation competence is reflection on decisions and actions during the translation process, as well as various process studies.