KLASIFIKASI EMOSI BERDASARKAN SUARA DENGAN METODE HIDDEN MARKOV MODEL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it