A Comparative Analysis of Deep Learning Models for Multi-class Speech Emotion Detection
Bibliographic record
Abstract
Abstract In today's digital age, where communication transcends traditional boundaries, the exploration of deep learning models for Speech Emotion Recognition (SER) holds immense significance. As we increasingly interact through digital platforms, understanding and interpreting emotions becomes crucial. Deep learning models, with their ability to autonomously learn intricate patterns and representations, offer unparalleled potential in enhancing the accuracy and efficiency of SER systems. This project delves into models for multi-class speech emotion recognition on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The RAVDESS dataset contains 1440 speech audio recordings from 24 professional actors, expressing 8 different emotions: neutral, calm, happy, sad, angry, fearful, surprise, and disgust. Models including Deep Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and ensembles were developed. Additionally, data augmentation through pitch shifting, noise injection, and a combination thereof expanded the dataset. Besides spectrogram inputs, handcrafted audio features like Mel Frequency Cepstral Coefficients (MFCCs), Chroma Short-time Fourier transform, root mean square, and zero crossing rate were experimented with as inputs to further boost model performance. The best-performing models were a Temporal Convolutional Network (TCN), achieving 96.88% testing accuracy, and a Gated Recurrent Unit (GRU) achieving 97.04% testing accuracy in classifying the 8 emotions, outperforming previous benchmark results on this dataset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".