Comparative Study of CNN Structures for Arabic Speech Recognition
Bibliographic record
Abstract
Speech recognition is an essential ability of human beings and is crucial for communication.Consequently, automatic speech recognition (ASR) is a major area of research that is increasingly using artificial intelligence techniques to replicate this human ability.Among these techniques, deep learning (DL) models attract much attention, in particular, convolutional neural networks (CNN) which are known due to their power to model spatial relationships.In this article, three CNN architectures that performed well in recognized competitions were implemented to compare their performance in Arabic speech recognition; these are the well-known models AlexNet, ResNet, and GoogLeNet.These models were compared based on a corpus composed of Arabic spoken digits collected from various sources, including messaging and social media applications, in addition to an online corpus.The architectures of AlexNet, ResNet, and GoogLeNet achieved respectively an accuracy of 86.19%, 83.46%, and 89.61%.The results show the superiority of GoogLeNet, and underline the potential of CNN architectures to model acoustic features of low-resource languages such as Arabic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".