ArticleJournal of healthcare engineering2022
Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A novel hybrid model for emotion detection in text through sequential and transformer-based approaches: LSTM enhanced RoBERTa (LER).Scientific reports · 2026Article
- Stacked convolutional neural network for emotion recognition using multi feature speech analysis.Scientific reports · 2025Article
- A computer vision approach for the classification of multi liver tumor using computed tomography scan.BMC medical imaging · 2025Article
- An improved facial emotion recognition system using convolutional neural network for the optimization of human robot interaction.Scientific reports · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron.Frontiers in artificial intelligence · 2024Article
- Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC).Scientific reports · 2023Article
- Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.Scientific reports · 2023Article
- A study on computer vision for facial emotion recognition.Scientific reports · 2023Article
- Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Content-Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm.Computational intelligence and neuroscience · 2023Article
- Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.Computational intelligence and neuroscience · 2022Article
- Text-Based Emotion Recognition Using Deep Learning Approach.Computational intelligence and neuroscience · 2022Article
- Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.Contrast media & molecular imaging · 2022Article
- Student and Lecturer Perceptions of the Use of an AI to Improve Communication Skills in Healthcare: An Interview Study.Journal of medical education and curricular developmentArticle
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8 authors.
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Abstract
Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/her aim and desire. While word analysis enables the speaker's request to be understood, other speech features disclose the speaker's mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model's performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset.
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