Speech Emotion Recognition Using Weighted Score Fusion for Low Resource Consumer Devices
Bibliographic record
Abstract
Deep learning techniques have significantly advanced machine performance in tasks traditionally dominated by human expertise. One such task is speech emotion recognition (SER), a crucial component of affective computing. Given the inherent complexity of recognizing emotions, even for humans, there is a persistent demand for models that are both robust and accurate, yet less complex for deployment on low-resource devices. Despite notable progress in SER systems, achieving high performance with low-parameter models remains a challenge. In this paper, we introduce a transformer-based multilayer SER model (TBWSC model) that employs a weight score configuration (WSC) across shallow, intermediate, and high-level learned features. This approach enhances SER performance by utilizing a transformer encoder to capture detailed acoustic feature representations, reducing the need for a deep network. Our model achieves significant improvements with just three layers, compared to traditional deep architectures. Additionally, we performed a comprehensive evaluation of our proposed TBWSC SER model to highlight the benefits of weighted fusion and complexity reduction. The model was tested on multiple datasets, including the emotional German speech dataset (EMODB), the Surrey audio-visual expressed emotion (SAVEE) database, and the Toronto emotional speech set (TESS). These datasets encompass emotional states such as happiness, sadness, neutrality, and anger. Our TBWSC model demonstrated superior performance, achieving an average accuracy of 81.82% on EMODB, 82.22% on SAVEE, and 99.65% on TESS. These results underscore the effectiveness of our weighted feature fusion approach and the potential for deploying efficient SER systems in low resource consumer devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.002 |
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".