Extreme Learning Machine for Automatic Language Identification Utilizing Emotion Speech Data
Why this work is in the frame
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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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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it