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Computer Vision and AI for Interactive Robotics

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Prof. Dr. Nieves Pavón-Pulido

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Guest Editor

Automation, Electrical Engineering and Electronic Technology Department, Industrial Engineering Technical School, Technical University of Cartagena, 30202 Cartagena, Spain
Interests: smart mobile robots; e-health systems; cloud and edge computing; human–robot interaction; artificial intelligence

Prof. Dr. Juan Antonio López Riquelme

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Guest Editor

Automation, Electrical Engineering and Electronic Technology Department, Industrial Engineering Technical School, Technical University of Cartagena, 30202 Cartagena, Spain
Interests: embedded systems; wireless sensor networks; e-health

Special Issue Information

Dear Colleagues,

The rise of robotics technology has led us into a new era of intelligence. Robots have been widely used in industrial manufacturing, medicine, transportation, agriculture, etc., greatly improving human production and living standards. Computer vision and artificial intelligence are important guarantees for achieving intelligent human–robot interaction. With the help of these advanced technologies, robots can recognize things, track their motion, comprehend spatial connections, and autonomously navigate and control complex and dynamic environments. In addition, the application of speech and emotion recognition in robots makes the interaction between humans and robots more intuitive and natural. With the advancement of machine learning, computer vision, natural language processing, and artificial intelligence technology, interactive robots will be more intelligent and efficient and provide humans with better services and experiences.

We invite authors to submit high-quality, original research papers in the field of interactive Robotics. The topics of interest in this Special Issue include, but are not limited to

Human–robot interaction;
Mobile robots;
Industrial robots;
Robots for medicine and healthcare;
Obstacle-avoidance robots;
Robot vision;
Robot design and control;
Robot perception;
Robot motion planning;
Visual robot for navigation;
Image processing based on robots;
Object detection based on robots and automation;
Robots and Cloud and Edge Computing.

Prof. Dr. Nieves Pavón-Pulido
Prof. Dr. Juan Antonio López Riquelme
Guest Editors

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Research

31 pages, 9940 KiB

Open AccessArticle

Combining Transformer, Convolutional Neural Network, and Long Short-Term Memory Architectures: A Novel Ensemble Learning Technique That Leverages Multi-Acoustic Features for Speech Emotion Recognition in Distance Education Classrooms

by Eman Abdulrahman Alkhamali, Arwa Allinjawi and Rehab Bahaaddin Ashari

Appl. Sci. 2024, 14(12), 5050; https://doi.org/10.3390/app14125050 - 10 Jun 2024

Cited by 2 | Viewed by 2084

Abstract

Speech emotion recognition (SER) is a technology that can be applied to distance education to analyze speech patterns and evaluate speakers’ emotional states in real time. It provides valuable insights and can be used to enhance students’ learning experiences by enabling the assessment of their instructors’ emotional stability, a factor that significantly impacts the effectiveness of information delivery. Students demonstrate different engagement levels during learning activities, and assessing this engagement is important for controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is their instructors’ emotional state. Accordingly, this study used deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entailed integrating transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance the SER. Feature extraction from audio data used Mel-frequency cepstral coefficients; chroma; a Mel spectrogram; the zero-crossing rate; spectral contrast, centroid, bandwidth, and roll-off; and the root-mean square, with subsequent optimization processes such as adding noise, conducting time stretching, and shifting the audio data. Several transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The preprocessing and data augmentation methodologies significantly enhanced the precision of the results, with accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% for the Ryerson Audio–Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio–Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets, respectively. Furthermore, it achieved 83% accuracy on another dataset created for this study, the Saudi Higher-Education Instructor Emotions dataset. The results demonstrate the considerable accuracy of this model in detecting emotions in speech data across different languages and datasets. Full article

(This article belongs to the Special Issue Computer Vision and AI for Interactive Robotics)

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