A Novel Classification Method with Cubic Spline Interpolation
Bibliographic record
Abstract
Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".