Automatic emotion recognition using auditory and prosodic indicative features
Bibliographic record
Abstract
In this paper, a new framework for the automatic recognition of human emotions from speech was proposed. Besides auditory indicative features, selected prosodic and voice quality parameters were optimally combined with Mel frequency coefficients to perform an automatic emotion classification. For this purpose, the Emotion Prosody Speech and Transcript database, a certified speech corpus, was used throughout this study. An extensive set of experiments have been carried out in order to assess the effectiveness of this original mixture of prosodic, perceptual and auditory features to perform the emotion recognition task. These features were selected by using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) on the basis of their ability of discrimination. The selected features were used by the front-end processing stage of a hybrid Gaussian Mixture Model and Support Vector Machines (GSVMs) to perform the emotion classification. The results showed the effectiveness of the proposed feature extraction framework to discriminate between different human emotions when the LDA-PCA-GSVM classifier was used.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".