Emotion Recognition Through Human Conversation Using Machine Learning Techniques

Sekhar, Ch.; Rao, M. Srinivasa; Nayani, A. S. Keerthi; Bhattacharyya, Debnath

doi:10.1007/978-981-15-9516-5_10

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…In order to better understand the psychological elements in musical instruments, investigators invented an innovative approach to categorize instruments used for music [19][20][21][22][23] . Researchers extract features, including MFCC and Chroma STFT, from libraries of music that are instrumental using Deep Learning (DL) approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Zhang,

Cai,

Zhang

2024

Environ. Soc. Psychol.

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In the current research, we integrate distinct learning modalities—Curriculum Learning (CL) and Reinforcement Learning (RL)—in an attempt to develop and optimize Music Emotion Recognition (MER) in piano performance. Classical approaches have never been successful when applied in the field of determining the degree of emotion in the music of the piano, owing to the substantial complexity required. Addressing this particular issue is the primary motivation for the present endeavour. In an approach that’s comparable to how human beings acquire information, it trains the RL agent CL in phases; such an approach improves the student’s learning model in understanding the diverse emotions expressed by musical compositions. A higher rating of performance can be achieved after learning the model to recognize emotions more effectively and precisely. A set of piano melodies with emotional content notes has been included in the EMOPIA repository for use when conducting the process of evaluation. In order to benchmark the proposed approach with different models, parameters including R2, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were deployed. Studies indicate that the recommended approach accurately recognizes the emotions expressed by piano-playing music. In challenging tasks like MER, the significance of implementing the CL paradigm with the RL has been emphasized using the outcomes mentioned earlier.

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Section: Literature Reviewmentioning

confidence: 99%

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Zhang,

Cai,

Zhang

2024

Environ. Soc. Psychol.

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“…In [27] constructed deep learning techniques for speech emotion recognition. The paper reviews databases used for training and evaluating speech emotion recognition models and explores various deep learning architectures employed in this domain.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Monitoring and Assessment System with Facial Landmark Estimation for Emotional Recognition in Work

Zhu

2023

IJRITCC

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The Model for Monitoring and Regulating Emotional States in the Work Environment based on Neural Networks and Emotion Recognition Algorithms presents an innovative approach to enhancing employee well-being and productivity by leveraging advanced technologies. This paper on the development of a system that utilizes neural networks and emotion recognition algorithms to monitor and interpret emotional cues exhibited by individuals in real-time within the work environment. With the uses of novel Directional Marker Controlled Facial Landmark (DMCFL) Emotion recognition algorithms are employed to analyze facial expressions, speech patterns, physiological data, and text-based communication to infer the emotional state of employees. Neural networks are then utilized to process this data and provide more sophisticated emotion classification and prediction. The emotional states are classified with the integrated Regression Logistics Classifier (RLC) model for classification. The analysis of the findings expressed that the real-time monitoring enables employers and supervisors to gain insights into the emotional well-being of employees, identifying patterns and potential issues. The system facilitates feedback and regulation mechanisms, allowing for personalized interventions and support tailored to individual emotional needs.

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“…Examples include techniques that specifically take advantage of machine learning's adaptability, like random forests [7] and support vector machines [8], both of which are frequently used in literature. Random forests tend to calculate more quickly, although support vector machines do better, according to studies [9].…”

Section: Related Workmentioning

confidence: 99%

Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

Mubeen¹,

Kulkarni²,

Tanpoco³

et al. 2022

Int. j. commun. netw. inf. secur.

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A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset.

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Emotion Recognition Through Human Conversation Using Machine Learning Techniques

Cited by 17 publications

References 7 publications

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Application and algorithm optimization of music emotion recognition in piano performance evaluation

Real-Time Monitoring and Assessment System with Facial Landmark Estimation for Emotional Recognition in Work

Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

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