KLASIFIKASI EMOSI BERDASARKAN SUARA DENGAN METODE HIDDEN MARKOV MODEL
Bibliographic record
Abstract
Technological developments make it easier for humans to interact with computers, such as speech recognition or speech-to-text. One of the speech recognition is to identify human emotions. To recognize a voice, extraction methods and classification algorithms are needed. Various studies combine various voice feature extraction methods and voice classification algorithms with MFCC and HMM methods. This study aims to classify emotions based on sound by combining the method of feature extraction of sound patterns using Mel Frequency Cepstral Coefficients (MFCC). Hidden Markov Model (HMM) method for speech classification. The data was used sourced from the Toronto Emotional Speech Set (TESS). Web-based interface design for Testing incoming voices and the results of the implementation of the MFCC and HMM algorithms get emotional sounds. The results of these emotions are displayed on the web speech recognition with the results of neutral emotions, happy emotions, sad emotions, fearful emotions, and angry emotions
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".