Emotion Recognition from Speech Utterances with Hybrid Spectral Features Using Machine Learning Algorithms
Bibliographic record
Abstract
Speech Emotion Recognition is always a complicated task in the domain of Speech Processing Research, though many research works have been done. The first and foremost challenge of SER is to selecting the Speech Emotion Database (Corpora), then extracting the related speech features and finally construct an appropriate Classification model. An effort is created during this work to discover the speech prosodies, spectral and combination of features with their dynamism to illustrate and classify the emotions of speech signal. The intrinsic or fine variations of speech samples are combined with the static delivery parameters within the Speech Emotion Recognition (SER) to refine the accuracy. The work in this paper, carried out the experiments on RAVDESS, IIITH IIITH-TEMD and our developed Database of native language DETL (Database for Emotions in Telugu Language) Speech Emotion Databases. This work extracted features like MFCC and Hybrid Features (MFCC+ΔMFCC+ΔΔMFCC) then finally applied those individual features and Combination of Features to different Classification models like SVM and MLP. We have got approximately 75%, 78% and 81% of accuracy for MLP with hybrid combination features on the above Databases respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".