Acoustic emotion recognition based on fusion of multiple feature-dependent deep Boltzmann machines
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a method to improve the classification recall of a deep Boltzmann machine (DBM) on the task of emotion recognition from speech. The task involves the binary classification of four emotion dimensions such as arousal, expectancy, power, and valence. The method consists of dividing the features of the input data into separate sets and training each set individually using a deep Boltzmann machine algorithm. Afterwards, the results from each set are fused together using simple fusion. The final fused scores are compared to scores obtained from support vector machine (SVM) classifiers and from the same DBM algorithm on the full feature set. The results show that the proposed method can improve the performance of classification of four dimensions and is suitable for classification of unbalanced data sets.
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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) | 0.000 | 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