Bibliographic record
Abstract
Given the fact that the length of the feature vector that is being used for the paralinguistic recognition of speech has exceeded some thousands, the importance of a sparse representation of a model becomes notable. The importance of a sparse representation is mainly due to the more interpretability, higher generalization capability, and numerically more efficiency of such a model. In this work, as an endeavor to search for a sparse representation of speech features used for paralinguistic speech modeling, we make use of the elastic net. As for the benchmark, we use the frameworks of the second audio/visual emotion challenge and the Interspeech 2012 speaker trait challenge. Also proposed in this work is the use of part-of-speech tags as syntactic features of speech for emotional speech recognition. Results of this work show that despite the relatively small number of features that is used for the modeling tasks, generalization capability of the suggested models is comparable to those of other models that use thousands of features and more elaborate learning algorithms.
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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) | 0.000 | 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".