Development of speech emotion recognition system using optimized convolutional neural network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Speech Emotion Recognition (SER) allows systems to interpret emotions in human speech, creating more natural and responsive interactions between people and machines. Due to the complex nature of emotion detection, several deep learning techniques have been utilized, yet limited research have focused on optimizing key hyperparameters of Convolutional Neural Network (CNN) for a more efficient system. Hence, this research optimized CNN with Mantis Search Algorithm (MSA) due to its ease of implementation, ability to preserve population diversity during the optimization process, ability to escape from the local optima and balance between exploration and exploitation operators. Audio data for four emotions: anger, fear, happiness and neutrality were acquired from Toronto Emotional Speech Set (TESS) available on Kaggle.com. The audio data were then converted into text using speech-to-text code and preprocessed using Natural Language Processing (NLP) techniques: tokenization, removal of stop words, lemmatization, removal of punctuations and lowercase conversion. Mantis Search Algorithm was then applied to optimize CNN for optimal selection of filter size and learning rate. The optimized CNN (MSA-CNN) was implemented using MATLAB R2023a software. The performance of the system was evaluated and compared with CNN classifier using False Positive Rate (FPR), Specificity (Spec), Sensitivity (Sen), Precision (Prec), Accuracy (Acc), and Recognition Time (RT). The optimized speech emotion recognition system showed improved values over CNN on all the metrics considered.
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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 | 0.000 | 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 it