KLASIFIKASI AUDIO UCAPAN EMOSIONAL MENGGUNAKAN MODEL LSTM
Bibliographic record
Abstract
Emosi adalah elemen penting dalam sintesis ucapan ekspresif manusia. Emosi ada bermacam macam, seperti sedih, marah, bahagia, dan bentuk emosi lainnya. Tujuan dari penelitian ini adalah membantu tenaga psikologi dengan menyajikan rekomendasi klasifikasi otomatis untuk ucapan emosional seseorang berdasarkan suara. Oleh karena itu, pada penelitian kali ini menggunakan dataset Toronto Emotional Speech (TESS) dengan jumlah data 2.800 serta jumlah kelas emosional sebanyak 7 dengan masing masing kelas memiliki banyak data sebanyak 400, akan dilakukan klasifikasi ucapan emosional seseorang. Makalah ini menggunakan metode Long Short-Term Memory (LSTM) dengan pembagian data latihan 70% dan data pengujian 30% dari total jumlah data. Dengan jumlah epoch 100 dan batch size 64 menghasilkan tingkat akurasi train 98,47% dan akurasi test 97,02% (best-fitting) serta ROC 99,9%. Kata kunci — Deep Learning, Klassifikasi, Long Short-Term Memory (LSTM), Toronto Emotional Speech Set (TESS), Ucapan Emosional.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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; both teacher heads agree on what is shown here.
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".