Extreme Learning Machine for Automatic Language Identification Utilizing Emotion Speech Data
Bibliographic record
Abstract
The technique used for recognizing a language by utilizing pronounced speech is called spoken Language Identification (LID). This field has a high significance in the interaction between human and computer. Besides, it can be implemented in several applications such as call centers, speaker diarization in multilingual environments, and in translation systems using a speech-to-speech manner. However, most studies that used LID systems are used and focused on neutral speech only. Moreover, the application of emotional speech in LID systems is crucial in real applications. Therefore, this study aims to investigate the performance of Extreme Learning Machine (ELM) in LID system by utilizing emotional speech. The system is evaluated based on two different languages (Germany and English language). This study has used the Berlin Emotional Speech Dataset (BESD) for the Germany language while the Ryerson Audio-Visual Dataset of Emotional Speech and Song (RAVDESS) for the English language. Four different evaluation scenarios (All Dataset (AD), Normal-Speech Dependent (N-SD), Gender-Female Dependent (G-FD), and Gender-Male Dependent (G-MD) scenario) have been conducted in order to evaluate the system. The experiments results have shown that the highest performance was achieved an accuracy of 99.08%, 100.00%, 98.22%, and 99.37% for AD, N-SD, G-FD, and G-MD scenario, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".