Emotional Speaker Recognition based on Machine and Deep Learning
Bibliographic record
Abstract
Speaker recognition is a method which recognise a speaker from characteristics of a voice. Speaker recognition technologies have been widely used in many domains. Most speaker recognition systems have been trained on normal clean recordings, however the performance of these speaker recognition systems tends to degrade when recognising speech which has emotions. This paper presents an emotional speaker recognition system trained using machine and deep learning algorithms using time, frequency and spectral features on emotional speech database acquired from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). We trained and compared the performance of five machine learning models (Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbor), and three deep learning models (Long Short-Term Memory network, Multilayer Perceptron, and Convolutional Neural Network). After the evaluation of the models, the deep neural networks showed good performance compared to machine learning models by attaining the highest accuracy of 92% outperforming the state-of-the-art models in emotional speaker detection from speech signals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".