A Multimodal Framework for Speech Emotion Recognition in Low-Resource Languages
Bibliographic record
Abstract
Speech emotion recognition (SER) plays a crucial role in enhancing human–computer interaction by identifying emotional states in speech. However, low-resource languages like Kazakh face challenges due to limited datasets and linguistic tools. To address this problem, we propose a novel multimodal framework, KEMO (Kazakh Emotion Multimodal Optimizer), which combines text-based semantic analysis and audio emotion recognition to leverage complementary features of linguistic and paralinguistic data. Using a Kazakh-translated version of the DAIR-AI (Contextualized Affect Representations for Emotion Recognition) dataset for text and the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset for audio, we have developed a system capable of classifying six emotions from text and eight emotions from audio. By integrating outputs from speech-to-text and audio-based recognition models with adaptive weighting, KEMO significantly improves the accuracy and robustness of emotion classification, providing an effective solution for SER in low-resource language scenarios.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".