Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks
Bibliographic record
Abstract
Speech emotion recognition (SER) is considered a pivotal area of research that holds significant importance in a variety of real-time applications, such as assessing human behavior and analyzing the emotional states of speakers in emergency situations. This paper assesses the capabilities of deep convolutional neural networks (CNNs) in this context. Both CNNs and Long Short-Term Memory (LSTM) based deep neural networks are evaluated for voice emotion identification. In our empirical evaluation, we utilize the Toronto Emotional Speech Set (TESS) database, which comprises speech samples from both young and old individuals, encompassing seven distinct emotions: anger, happiness, sadness, fear, surprise, disgust, and neutrality. To augment the dataset, variations in voice are introduced along with the addition of white noise. The empirical findings indicate that the CNN model outperforms existing studies on SER using the TESS corpus, yielding a noteworthy 21% improvement in average recognition accuracy. This work underscores SER’s significance and highlights the transformative potential of deep CNNs for enhancing its effectiveness in real-time applications, particularly in high-stakes emergency situations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".