Human emotion detection through speech and facial expressions

Kudiri, Krishna Mohan; Said, Abas Md; Nayan, M Yunus

doi:10.1109/iccoins.2016.7783240

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…Another widely used technique is speech [ 49 ]. It contains information not only limited to what the person is saying but also holds the information of the speaker including their emotions, real-time interactions, and various meanings [ 50 ].…”

Section: Sensors For Human Emotion Recognitionmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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Section: Sensors For Human Emotion Recognitionmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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“…Interdisciplinary collaborations of social scientists and computer scientists would allow to follow multimodal approaches when analyzing workplace gossip. Considering the technological advancements within the past years such as automated facial expression (e.g., Kudiri et al, 2016) or automated speech recognition (e.g., Milde et al, 2021), future research could identify non-verbal and paralinguistic cues of workplace gossip valence. In our context, we focused primarily on verbal behavior.…”

Section: Implications For Interdisciplinary Research Collaborationsmentioning

confidence: 99%

Capturing Workplace Gossip as Dynamic Conversational Events: First Insights From Care Team Meetings

Begemann¹,

Lübstorf²,

Meinecke³

et al. 2021

Front. Psychol.

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Even though gossip is a ubiquitous organizational behavior that fulfils important social functions (e.g., social bonding or emotion venting), little is known about how workplace gossip and its functions unfold in situ. To explore the dynamic nature and social embeddedness of workplace gossip, we develop a behavioral annotation system that captures the manifold characteristics of verbal gossip behavior, including its valence and underlying functions. We apply this system to eight elderly care team meetings audio- and videotaped in the field, yielding a sample of N = 4,804 annotated behaviors. On this empirical basis, we provide first insights into the different facets and functions of workplace gossip in real-life team interactions. By means of lag sequential analysis, we quantify gossip patterns that point to the temporal and structural embeddedness of different types of workplace gossip expressions. Though exploratory, these findings help establish workplace gossip as a dynamic conversational event. We discuss future interdisciplinary research collaborations that behavioral observation approaches offer.

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“…Facial expression and speech are the most direct emotional information, and Kudiri et al acquired them in dialogue to recognize eight basic emotions (anger, sadness, happiness, boredom, disgust, fear, surprise, and neutral) using support vector machine (SVM) [ 11 ]. Wollmer et al recognized the valence and activation by applying bi-directional long short-term memory (BLSTM) to them in the video [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment

Choi

Cho

2018

Sensors

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The cyber-physical system (CPS) is a next-generation smart system that combines computing with physical space. It has been applied in various fields because the uncertainty of the physical world can be ideally controlled using cyber technology. In terms of environmental control, studies have been conducted to enhance the effectiveness of the service by inducing ideal emotions in the service space. This paper proposes a CPS control system for inducing emotion based on multiple sensors. The CPS can expand the constrained environmental sensors of the physical space variously by combining the virtual space with the physical space. The cyber space is constructed in a Unity 3D space that can be experienced through virtual reality devices. We collect the temperature, humidity, dust concentration, and current emotion in the physical space as an environmental control elements, and the control illumination, color temperature, video, sound and volume in the cyber space. The proposed system consists of an emotion prediction module using modular Bayesian networks and an optimal stimulus decision module for deriving the predicted emotion to the target emotion based on utility theory and reinforcement learning. To verify the system, the performance is evaluated using the data collected from real situations.

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Human emotion detection through speech and facial expressions

Cited by 13 publications

References 16 publications

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Capturing Workplace Gossip as Dynamic Conversational Events: First Insights From Care Team Meetings

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment

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