Human-machine Communication Research Articles

EEG SIGNAL-DRIVEN HUMAN–COMPUTER INTERACTION EMOTION RECOGNITION MODEL USING AN ATTENTIONAL NEURAL NETWORK ALGORITHM

The level of human–machine interaction experience is raising its bar as artificial intelligence develops quickly. An important trend in this application is the improvement of the friendliness, harmony, and simplicity of human–machine communication. Electroencephalogram (EEG) signal-driven emotion identification has recently gained popularity in the area of human–computer interaction (HCI) because of its advantages of being simple to extract, difficult to conceal, and real-time differences. The corresponding research is ultimately aimed at imbuing computers with feelings to enable fully harmonic and organic human–computer connections. This study applies three-dimensional convolutional neural networks (3DCNNs) and attention mechanisms to an environment for HCI and offers a dual-attention 3D convolutional neural networks (DA-3DCNNs) model from the standpoint of spatio-temporal convolution. With the purpose of extracting more representative spatio-temporal characteristics, the new model first thoroughly mines the spatio-temporal distribution information of EEG signals using 3DCNN, taking into account the temporal fluctuation of EEG data. Yet, a dual-attention technique based on EEG channels is utilized at the same time to strengthen or weaken the feature information and understand the links between various brain regions and emotional activities, highlighting the variations in the spatiotemporal aspects of various emotions. Finally, three sets of experiments were planned on the DEAP dataset for cross-subject emotion classification experiments, channel selection experiments, and ablation experiments, respectively, to show the validity and viability of the DA-3DCNN model for HCI emotion recognition applications. The outcomes show that the new model may significantly increase the model’s accuracy in recognizing emotions, acquire the spatial relationship of channels, and more thoroughly extract dynamic information from EEG.

Is artificial intelligence more persuasive than humans? A meta-analysis

Abstract The rapid deployment of artificial intelligence (AI) technology has enabled AI agents to take on various roles as communicators, such as virtual assistants, robot journalists, and AI doctors. This study meta-analyzed 121 randomized experimental studies (N = 53,977) that compared the effects of AI and human agency on persuasion outcomes, including perceptions, attitudes, intentions, and behaviors. The results showed that AI agents were as persuasive as humans in terms of overall persuasion outcomes. With regard to different types of outcomes, AI was less effective than humans at shaping behavioral intentions, but did not differ significantly from humans in eliciting perceptions, attitudes, or actual behaviors. Additionally, heterogeneous patterns were observed for different roles of AI communicators, directions of communication, experimental settings, and demographic segments. The implications of these findings for human–machine communication and persuasion in the era of AI are discussed.

Beyond ownership: Human–robot relationships between property and personhood

As artificial intelligence (AI)-enabled social robots continue to enter our lifeworlds, we will need to grapple with challenges to assumptions about our relationship to and even with these technological objects. This essay works at the intersection of social robotics, legal studies, and human–machine communication to explore the concept of ownership in human–machine relations. In particular, we draw on more-than-human approaches to ask: To what extent should (need) we retire the concept of ownership in the context of AI-enabled social robots? With a particular emphasis on companion robots, we explore alternatives to the ownership modality by investigating concepts such as personhood, a degrees-of-relationship perspective, and a situational approach to understanding human–robot relationships. The goal is to re-imagine human–robot relationships beyond legal confinements by engaging a pragmatic perspective that supplements existing philosophical approaches. We conclude the paper by discussing practical implications of our proposed perspective.

Artificial intelligence and human communication: A systematic literature review

The advancement of artificial intelligence has enhanced communication and contributed to the flow of information in recent years. This review attempts to report on the findings of a systematic literature review on artificial intelligence and human communication. The review focused on four databases— Elsevier, Google Scholar, Oxford and Sage. In order to refine the results, inclusion and exclusion criteria were defined with the purpose of eliminating all publications which came across as clearly irrelevant. After applying the protocol and the analysis model, a corpus of 21 papers was obtained between May and June, 2023. The results show that with the increasing availability of monitoring data and the recent advances in computing platforms, artificial intelligence has become a tool for network operators to automate communication. In addition, artificial intelligence-based technologies such as machine learning, human machine communication and computer mediated communication have demonstrated superhuman capabilities in solving some real world problems.

An Interactional Account of Empathy in Human-Machine Communication

Efforts to develop empathetic agents, or systems capable of responding appropriately to emotional content, have increased as the deployment of such systems in socially complex scenarios becomes more commonplace. In the context of human-machine communication (HMC), the ability to create the perception of empathy is achieved in large part through linguistic behavior. However, studies of how language is used to display and respond to emotion in ways deemed empathetic are limited. This article aims to address this gap, demonstrating how an interactional linguistics informed methodological approach can be applied to the study of empathy in HMC. We present an analysis of empathetic response strategies in HMC and examine how these diverge from the practices employed in human-human dialogue. The specific challenges encountered by current systems are reviewed and their implications for future work on HMC considered.

Archipelagic Human-Machine Communication: Building Bridges amidst Cultivated Ambiguity

In this commentary, I call for maintaining the archipelagic character of human-machine communication (HMC). Utilizing the metaphor of the archipelago or a chain of connected islands indicates that HMC entails a variety of islands differing in shape, size, location, and proximity to one another. Rather than aiming for conceptual unity and definitional homogeneity, I call for embracing a cultivated ambiguity related to HMC key concepts. Ambiguity in the sense of allowing these concepts to be flexible enough to be explored in different contexts. Cultivated in the sense of demanding resonance across individual studies and theoretical lineages to allow for cumulative and collaborative theorizing. My hope is that HMC scholars can continue to build bridges that traverse the paradigmatic, methodological, theoretical, and technological archipelago of HMC.

Defining Dialogues: Tracing the Evolution of Human-Machine Communication

This introduction to the volume discusses the evolving field of Human-Machine Communication (HMC), drawing on insights from the philosophy of science. We explore critical debates in the field, underscoring the importance of challenging assumptions, embracing interfield work, and fostering dialogue in shaping our understanding of HMC. Moreover, we celebrate the vibrant collaboration between disciplines that drives progress in HMC. This piece serves as an invitation to join the exploration of this collection and contribute to shaping the future of HMC.

Disentangling Two Fundamental Paradigms in Human-Machine Communication Research: Media Equation and Media Evocation

In this theoretical paper, we delineate two fundamental paradigms in how scholars conceptualize the nature of machines in human-machine communication (HMC). In addition to the well-known Media Equation paradigm, we distinguish the Media Evocation paradigm. The Media Equation paradigm entails that people respond to machines as if they are humans, whereas the Media Evocation paradigm conceptualizes machines as objects that can evoke reflections about ontological categories. For each paradigm, we present the main propositions, research methodologies, and current challenges. We conclude with theoretical implications on how to integrate the two paradigms, and with a call for mixedmethod research that includes innovative data analyses and that takes ontological classifications into account when explaining social responses to machines.

Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication

Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication.

Boundary Regulation Processes and Privacy Concerns With (Non-)Use of Voice-Based Assistants

An exemplar of human-machine communication, voice-based assistants (VBAs) embedded in smartphones and smart speakers simplify everyday tasks while collecting significant data about users and their environment. In recent years, devices using VBAs have continued to add new features and collect more data—in potentially invasive ways. Using Communication Privacy Management theory as a guiding framework, we analyze data from 11 focus groups with 65 US adult VBA users and nonusers. Findings highlight differences in attitudes and concerns toward VBAs broadly and provide insights into how attitudes are influenced by device features. We conclude with considerations for how to address boundary regulation challenges inherent in human-machine interactions.

Triggered by Socialbots: Communicative Anthropomorphization of Bots in Online Conversations

This article examines communicative anthropomorphization, that is, assigning of humanlike features, of socialbots in communication between humans and bots. Situated in the field of human-machine communication, the article asks how socialbots are devised as anthropomorphized communication companions and explores the ways in which human users anthropomorphize bots through communication. Through an analysis of two datasets of bots interacting with humans on social media, we find that bots are communicatively anthropomorphized by directly addressing them, assigning agency to them, drawing parallels between humans and bots, and assigning emotions and opinions to bots. We suggest that socialbots inherently have anthropomorphized characteristics and affordances, but their anthropomorphization is completed and actualized by humans through communication. We conceptualize this process as communicative anthropomorphization.

PERCEPTION OF HUMAN EXPRESSION, AGE AND GENDER RELATED VARIATIONS OF EMOTIONRECOGNITION BASED ON LIVE CAMERA IMAGES IN DEEP LEARNING USING PYTHON

Now-a-days online marketing is rapidly increasing. As a growth, we capture the customer images through web camera. Depending on their age, gender and expression of mood easily continue our product marketing in online. Automated face detection methods are substantial for facial identification, head position analysis, and human-computer communication. Face detection is a method for detecting and analyzing a human face in a webcam image. Identifying gender and oldness has now established itself as a key step in numerous human-machine communications. It can be used in advertising, security, and all other areas where gender and age recognition help them better to do the task given to them. Gender, oldness and mood of user forecast is the initial tool for any system which utilize it to scale their performance through personalized communication with the user of certain age and gender and mood of customers to improve our business online itself with high preference.

FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks.

Keyword spotting (KWS) systems are used for human-machine communications in various applications. In many cases, KWS involves a combination of wake-up-word (WUW) recognition for device activation and voice command classification tasks. These tasks present a challenge for embedded systems due to the complexity of deep learning algorithms and the need for optimized networks for each application. In this paper, we propose a depthwise separable binarized/ternarized neural network (DS-BTNN) hardware accelerator capable of performing both WUW recognition and command classification on a single device. The design achieves significant area efficiency by redundantly utilizing bitwise operators in the computation of the binarized neural network (BNN) and ternary neural network (TNN). In a complementary metal-oxide semiconductor (CMOS) 40 nm process environment, the DS-BTNN accelerator demonstrated significant efficiency. Compared with a design approach where BNN and TNN were independently developed and subsequently integrated as two separate modules into the system, our method achieved a 49.3% area reduction while yielding an area of 0.558 mm2. The designed KWS system, which was implemented on a Xilinx UltraScale+ ZCU104 field-programmable gate array (FPGA) board, receives real-time data from the microphone, preprocesses them into a mel spectrogram, and uses this as input to the classifier. Depending on the order, the network operates as a BNN or a TNN for WUW recognition and command classification, respectively. Operating at 170 MHz, our system achieved 97.1% accuracy in BNN-based WUW recognition and 90.5% in TNN-based command classification.

Speech production under uncertainty: how do job applicants experience and communicate with an AI interviewer?

Abstract Theories and research in human–machine communication (HMC) suggest that machines, when replacing humans as communication partners, change the processes and outcomes of communication. With artificial intelligence (AI) increasingly used to interview and evaluate job applicants, employers should consider the effects of AI on applicants’ psychology and performance during AI-based interviews. This study examined job applicants’ experience and speech fluency when evaluated by AI. In a three-condition between-subjects experiment (N = 134), college students had an online mock job interview under the impression that their performance would be evaluated by a human recruiter, an AI system, or an AI system with a humanlike interface. Participants reported higher uncertainty and lower social presence and had a higher articulation rate in the AI-evaluation condition than in the human-evaluation condition. Through lowering social presence, AI evaluation increased speech rate and reduced silent pauses. Findings inform theories of HMC and practices of automated recruitment and professional training.

人机互动中的自我表露与隐私计算动态关系研究

随着可交流的AI技术发展,人与媒介的关系将被重新定义和塑造。本文以隐私计算理论为视角,通过调查1126名AI聊天机器人用户,探究其在人机互动中的感知收益和感知隐私风险与自我表露意向之间的关系。结果发现,人们对聊天机器人有着开放的情绪表露意愿和谨慎的身份表露意愿。人们在身份表露时理性地权衡隐私风险与预期收益;但在情绪表露时,人们不受感知隐私风险影响,大胆地向机器人敞开心扉。本研究有助于理解未来人机交流的走向趋势。

Speech Emotion Recognition Using Deep Learning

Abstract: Due to different technical developments, speech signals have evolved into a kind of human-machine communication in the digital age. Recognizing the emotions of the person behind his or her speech is a crucial part of Human-Computer Interaction (HCI). Many methods, including numerous well-known speech analysis and classification algorithms, have been employed to extract emotions from signals in the literature on voice emotion recognition (SER). Speech Emotion Recognition (SER) approaches have become obsolete as the Deep Learning concept has come into play. In this paper, the algorithm for identifying speech-based emotions is implemented using deep learning. It also provides an overview of deep learning methodologies and examines some recent research that makes use of these methods. It makes use of a dataset of various emotional voices and then aids in the identification of that emotion. It will be beneficial for the computers or robots to understand humans more clearly and function in accordance with it.

Study on emotion recognition bias in different regional groups

Human-machine communication can be substantially enhanced by the inclusion of high-quality real-time recognition of spontaneous human emotional expressions. However, successful recognition of such expressions can be negatively impacted by factors such as sudden variations of lighting, or intentional obfuscation. Reliable recognition can be more substantively impeded due to the observation that the presentation and meaning of emotional expressions can vary significantly based on the culture of the expressor and the environment within which the emotions are expressed. As an example, an emotion recognition model trained on a regionally-specific database collected from North America might fail to recognize standard emotional expressions from another region, such as East Asia. To address the problem of regional and cultural bias in emotion recognition from facial expressions, we propose a meta-model that fuses multiple emotional cues and features. The proposed approach integrates image features, action level units, micro-expressions and macro-expressions into a multi-cues emotion model (MCAM). Each of the facial attributes incorporated into the model represents a specific category: fine-grained content-independent features, facial muscle movements, short-term facial expressions and high-level facial expressions. The results of the proposed meta-classifier (MCAM) approach show that a) the successful classification of regional facial expressions is based on non-sympathetic features b) learning the emotional facial expressions of some regional groups can confound the successful recognition of emotional expressions of other regional groups unless it is done from scratch and c) the identification of certain facial cues and features of the data-sets that serve to preclude the design of the perfect unbiased classifier. As a result of these observations we posit that to learn certain regional emotional expressions, other regional expressions first have to be “forgotten”.

Contributors

Contributors

Emotional Artificial Intelligence as a Tool for Human-Machine Interaction

Introduction. One of the trends of global importance is the artificial intelligence (AI) and its innovations. One of such innovations has become emotional artificial intelligence (emotional AI/AI), it is called a revolutionary technology that can identify human emotions, process them in a timely manner and react “properly”. Experts consider emotional AI as an instrument that provides emotionally-oriented human–machine communication. The article discusses the specifics of emotional AI, achievements, potential opportunities, development prospects.Methodology and sources. The methodology of philosophical, socio-psychological, comparative and interdisciplinary approaches is used. The sources used in the article are: special literature of domestic and foreign authors (B. Goertzel, D. Goleman, R. Picard,D.I. Dubrovsky, E.M. Proidakov) scientific research, publications and websites devoted to emotional artificial intelligence, and its features (Aliya Green Emotional artificial intelligence: changing the human world for the better).Results and discussion. The relevance of the topic of emotional AI determined the need to refer to the concept of ”emotional intelligence” (EI) as the basic basis of emotional artificial intelligence, which allowed us to show the essential characteristics of human emotional intelligence, its difference from AI. Emotional artificial intelligence is an innovation of modern AI, its main actors are anthropomorphic robots, text, voice chatbots and video bots, which are already actively demonstrating to the public their knowledge and skills in the field of psychology of emotions, which are being improved within the framework of the current AI.Conclusion. Currently, there is a gradual process of teaching emotional AI to interact with a person, and although these achievements are not great yet, EII is gradually developing in accordance with the challenges of new realities within the specifics of modern applied AI. However, in the digital age, human–machine and machine-to-human communication is an interconnected process that should be aimed at building both utilitarian–useful and partnership relations in the practices of their interaction, which meets the requirements of the era and leads to further progress of AI – to the creation of a new, common AI – “human AI the level” which is supposed to greatly expand the capabilities of a person and society as a whole.

Gender identity and influence in human-machine communication:A mixed-methods exploration

The advancement of conversational technologies stimulates new research agenda on the patterns, norms, and social impacts of human-machine communication (HMC) as a novel process. Conversational agents (CAs), a prevalent example of machines that communicate with users directly, are usually depicted as females in assisting roles. This study intends to explore empirical evidence of how “gendered” technologies might influence HMC and potentially reinforce gender stereotyping in human-human communication. We applied a mixed-methods approach to explore users' gender-related responses and evaluations in the interaction with CAs. First, we observed unrestricted interactions between 36 human participants and Amazon Alexa in a laboratory and qualitatively analyzed the transcripts to detect gendered communication cues. We then conducted a 2 × 3 (participant gender: female vs. male; CA gender: female vs. male vs. neutral) online experiment where 250 participants interacted with a customized chatbot created by the researcher. Results showed participants’ different emotions/tones, engagement, (non)accommodation, as well as credibility, attraction, and likeability evaluations between human-CA gender pairs.