Emotion recognition from speech: WOC-NN and class-interaction
Bibliographic record
Abstract
This study represents an extension work of the Weighted Ordered Classes-Nearest Neighbors (WOC-NN), a class-similarity based method introduced in our previous work [1]. WOC-NN computes similarities between a test instance and a class pattern of each emotion class in the likelihood space. An emotion class pattern is a representation of its ranked neighboring classes weighted according to their discrimination capability. In this study the class ranks weights are normalized inside each class pattern. We have also studied a new model of distance pattern based on a double class ranks introduced in order to take into account the interaction between the rank variables. The performance of the system based on double class ranks exceeds those based on a single class rank. Furthermore, using likelihood score rank of all class models in the decision rule of WOC-NN adds valuable information for data discrimination. The experiments on FAU AIBO corpus show that WOC-NN approach enhances the relative performance with 5.1% compared to Bayes decision rule. Also, the obtained result outperforms the state-of-the art ones.
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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) | 0.016 | 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; both teacher heads agree on what is shown here.
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".