Speech emotion recognition using long-term average spectrum
Bibliographic record
Abstract
Abstract Automatic speech emotion recognition has become an important research subject in the area of speech signal processing. The performance of classification algorithms depends on the features extracted from speech. In this work, a new framework for emotion recognition is proposed based on the long-term average spectrum (LTAS). Our framework is evaluated through a comparative study, where classifiers such as artificial neural network, K-nearest neighbours, logistic regression, Bayesian algorithms, tree-based logistics, and support vector machine were used. The framework was experimentally tested using the well-known Toronto Emotional Speech Set database, and the results were compared against state-of-the-art alternatives, using mel frequency cepstral coefficients, filter bank energies, and chroma coefficient speech coding, on this database. Comparative experiments showed that the use of LTAS achieved higher performance, with accuracies of 96–99% in terms of correct classification of speech emotion, compared with the best performance of 97% for the state-of-the-art alternatives. Different sampling frequencies were used to extract LTAS, and the classifiers were tested individually. The main contribution of this work is to demonstrate that the new framework using LTAS significantly reduces the number of parameters down to 87.5 values per s (approximately), as opposed to the 1,200 values used in the best-performing state-of-the-art alternatives; this means that the process of feature extraction is significantly reduced and the performance in terms of correct classification is improved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".