Combined CNN-LSTM for Enhancing Clean and Noisy Speech Recognition
Bibliographic record
Abstract
This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) approach for Automatic Speech Recognition (ASR) using deep learning techniques on the Aurora-2 dataset. The dataset includes both clean and multi-condition modes, encompassing four noise scenarios : subway, babble, car, and exhibition hall, each evaluated at different signal-to-noise ratios (SNRs), and clean condition, and the results are compared with those from the ASC-10 dataset and the ESC-10 dataset. The problem addressed is the need for robust ASR models that perform well in both clean and noisy environments. The aim of utilizing the CNN-LSTM architecture is to enhance the recognition performance by combining the strengths of CNNs and LSTMs, rather than relying on either CNNs or LSTMs alone. Experimental results demonstrate that the combined CNN-LSTM model achieves superior classification performance, in clean environments on the Aurora2 dataset, attaining an accuracy of 97.96%, surpassing the individual CNN and LSTM models, which achieved 97.21% and 96.06%, respectively. In noisy conditions, the hybrid model also outperforms the standalone models, with an accuracy of 90.72%, compared to 90.12% for CNN and 86.12% for LSTM. These findings indicate that the CNN-LSTM model is more effective in handling various noise conditions and improving overall ASR accuracy.
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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 | 0.000 | 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 | 0.001 | 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".