Bidirectional Long short-term memory and Recurrent Neural Network model for speech recognition
Bibliographic record
Abstract
Speech-to-text is essential as it converts spoken words to text, thus making it easy to store. It has several components; from a basic model, it is viewed in four stages; Signal pre-processing, feature extraction, feature selection, and modeling. Several works of literature have been documented on improving and achieving better results in speech recognition. However, works remains in resolving the issue of word error rate and accuracy on continuous input stream without increasing the required bandwidth. This research evaluates recurrent neural networks, long short-term memory neural networks, gated recurrent units, and bi-directional long short-term memory. It further tests the signal’s performance after introducing bias to the long short-term memory. This research then proposes a model bi-directional long short-term memory recurrent neural network. Experimental results demonstrate that even with a bias of one on long short-term memory, the bidirectional long short-term memory recurrent neural network model still achieves better results with a word error rate of 8.92%, accuracy of 91.08% and mean edit distance of 0.1910 using the Libri speech training dataset. Future work will evaluate the use of the transformer models in the reduction of the word error rate and accuracy on a continuous input stream.
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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 | 0.000 |
| Open science | 0.001 | 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 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".