DeepVoice: An End-to-End Speaker Recognition System Leveraging Convolutional and Recurrent Neural Networks for Robust Voice Identification
Bibliographic record
Abstract
In this work, we present DeepVoice, a comprehensive speaker recognition system that uses Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) to improve the accuracy of voice-based identification systems. In contrast to conventional speaker recognition techniques that depend on segmented processing pipelines and manual feature engineering, DeepVoice uses automated feature extraction and temporal sequence modelling to expedite the recognition process. Because the system can handle both gender categorisation and speaker identification, it offers a flexible option for speech analysis jobs. Tests on a heterogeneous dataset with male and female speech samples show that DeepVoice outperforms traditional methods with 91.23% speaker identification accuracy and 98.56% gender classification accuracy with a validation loss of 0.21. These outcomes demonstrate how accurate and reliable the system is, which makes it a viable option for practical speaker identification applications.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".