Multimodal Emotion Recognition: An Integrated Approach using Facial, Audio and Text Analysis
Bibliographic record
Abstract
In an era among the increasing interaction between humans and technology, the capacity to accurately recognize and the ability to react to human emotions has become crucial particularly in fields such as medical care, customer support, and personal assistance, where empathetic responses can greatly enhance user experience and effectiveness. This study presents an integrated emotion recognition system that leverages various modalities, including facial expressions, audio, and text, to determine a user's emotional state. The system processes input data from diverse sources—such as the Toronto Emotion Speech Set (TESS) and FER datasets—through an architecture involving data cleaning, preprocessing, and feature extraction. The proposed approach utilizes MobileNetV2 for facial analysis using Convolutional Neural Networks (CNN) with the FER dataset, a Multilayer Perceptron (MLP) with Librosa for audio analysis using the TESS dataset, and Natural Language Processing (NLP) combined with Linear Regression for text analysis. Data preprocessing ensures the caliber and reliability after which the input data is divided into training and testing and testing datasets. Tranining of the model is done using a mix of MobileNetV2, MLP, and NLP algorithms, with the final model employing an ensemble-based approach to combine the outputs of facial, audio, and textual analyses into a unified emotion prediction. A compilation of the model is followed by training and evaluation, optimizing the model’s performance by saving the weights and parameters. The trained model receives real-time inputs—facial images, audio clips, or text messages— processed through a user-friendly interface to recognize emotions effectively. The study aims to create an efficient and accessible solution to real-time emotion detection, integrating machine learning models to enhance user interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".