Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
Bibliographic record
Abstract
Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhance SER accuracy and generalization through innovative deep learning models. Despite its importance in various fields like human–computer interaction and mental health diagnosis, accurately identifying emotions from speech can be challenging due to differences in speakers, accents, and background noise. The work proposes two innovative deep learning models to improve SER accuracy: a CNN-LSTM model and an Attention-Enhanced CNN-LSTM model. These models were tested on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), collected between 2015 and 2018, which comprises 1440 audio files of male and female actors expressing eight emotions. Both models achieved impressive accuracy rates of over 96% in classifying emotions into eight categories. By comparing the CNN-LSTM and Attention-Enhanced CNN-LSTM models, this study offers comparative insights into modeling techniques, contributes to the development of more effective emotion recognition systems, and offers practical implications for real-time applications in healthcare and customer service.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".