A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
Bibliographic record
Abstract
As a fundamental element of human-computer interaction, speech recognition-the ability of software systems to identify and interpret human language-has garnered immense attention in recent years.This review offers a rigorous examination of machine learning techniques deployed for optimizing speech recognition capabilities.It delves into the utilization of prominent datasets-such as Librispeech, Timit, and Voxforge-in speech recognition research and underscores their significant contributions to enhancing the accuracy of recognition systems.Furthermore, the efficacy of assorted classification techniques-including deep neural networks (DNN), convolutional neural networks (CNN), support vector machines (SVM), and random forests (RF)-is evaluated in the context of voice recognition.It is observed that Mel-Frequency Cepstral Coefficients (MFCC) often render superior discriminatory abilities in human voice recognition trials.This review stands to provide valuable insights for both researchers and professionals active in the field of speech recognition, thereby paving the way for future advancements in this domain.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".