ASER: An Exhaustive Survey for Speech Recognition based on Methods, Datasets, Challenges, Future Scope
Bibliographic record
Abstract
AI has been used to process the data for decision-making, problem-solving, interaction with humans and to understand human's feelings, emotions and their behavior.In today's world, communication between humans takes place digitally, so human's emotions play a very important role for communication as well as detection and analysis.Although there are many surveys related to emotions from speech already done, selecting appropriate datasets and methods are challenging tasks.This survey will primarily concentrate on efficient techniques, including Machine Learning, Deep Learning, and transformer-based approaches, while also providing brief descriptions of existing challenges and outlining future prospects.Additionally, this paper provides a comparative analysis of various datasets and techniques employed by researchers.After conducting the survey, we discovered that deep learning and transformer-based techniques are more effective and yield superior performance results.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.017 |
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".