Model Klasifikasi Emosi Berdasarkan Suara Manusia dengan Metoode Multilater Perceptron
Bibliographic record
Abstract
Teknologi interaksi manusia dengan komputer sudah semakin berkembang, misalnya pengenalan suara atau speech recognition. Salah satu kegunaan dari pengenalan suara adalah untuk mengenali emosi manusia. Komputer dapat mengenali dan mengklasifikasi emosi manusia berdasarkan suara. Sudah banyak penelitian terkait dengan berbagam metode ekstraksi ciri dan klasifikasi namun hasilnya masih belum mendekati sempurna. Adapun metode ekstraksi ciri menggunakan Mel Frequency Ceptral Coefficient (MFCC). Data yang digunakan adalah data sekunder yang bersumber dari Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Model sistem akan dapat mengenali 8 jenis emosi yaitu netral, tenang, senang, sedih, marah, takut, jijik dan terkejut. Hasil dari model didapatkan akurasi untuk emosi netral sebesar 98%, emosi tenang sebesar 97%, emosi senang sebesar 94%, emosi sedih sebesar 97%, emosi marah sebesar 97%, emosi takut sebesar 94%, emosi jijik sebesar 97% dan emosi terkejut sebesar 96%. Sehingga hasil akurasi rata-rata dari model yang telah dibuat sebesar 96%
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".