Exploring convolutional neural network structures and optimization techniques for speech recognition
Bibliographic record
Abstract
Recently, convolutional neural networks (CNNs) have been shown to outperform the standard fully connected deep neu-ral networks within the hybrid deep neural network / hidden Markov model (DNN/HMM) framework on the phone recogni-tion task. In this paper, we extend the earlier basic form of the CNN and explore it in multiple ways. We first investigate sev-eral CNN architectures, including full and limited weight shar-ing, convolution along frequency and time axes, and stacking of several convolution layers. We then develop a novel weighted softmax pooling layer so that the size in the pooling layer can be automatically learned. Further, we evaluate the effect of CNN pretraining, which is achieved by using a convolutional version of the RBM. We show that all CNN architectures we have in-vestigated outperform the earlier basic form of the DNN on both the phone recognition and large vocabulary speech recog-nition tasks. The architecture with limited weight sharing pro-vides additional gains over the full weight sharing architecture. The softmax pooling layer performs as well as the best CNN with the manually tuned fixed-pooling size, and has a potential for further improvement. Finally, we show that CNN pretrain-ing produces significantly better results on a large vocabulary speech recognition task.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".