Literature survey on development of a model for detecting emotions using CNN and LSTM
Bibliographic record
Abstract
This paper explores the utilization of three major datasets, SAVEE, Toronto Emotion Speech Set (TESS), and CREMA-D, which together contain a substantial repository of 75,000 samples. These datasets cover a broad spectrum of human emotions, from anger, sadness, fear, and disgust to calm, happiness, neutral states, and surprise, mapped to numerical labels from 1 to 8, respectively. The primary objective is to develop a real-time deep learning system specifically tailored for emotion recognition using speech inputs from a PC microphone. This system aims to create a robust model capable of not only capturing live speech but also analyzing audio files in detail, allowing for the classification of specific emotional states. To achieve this, the Long Short-Term Memory (LSTM) network architecture, a specialized form of Recurrent Neural Network (RNN), was chosen for its proven accuracy in speech-centered emotion recognition tasks. The model was rigorously trained using the RAVDESS dataset, comprising 7,356 distinct audio files, with 5,880 files carefully selected for training to enhance accuracy and improve the model's effectiveness in detecting emotions across diverse speech samples. The resulting model achieved a training dataset accuracy of 83%, marking a substantial milestone in advancing speech-based emotion recognition systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".