Rapid and effective speaker adaptation of convolutional neural network based models for speech recognition
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Bibliographic record
Abstract
Recently, we have proposed a novel fast adaptation method for the hybrid DNN-HMM models in speech recognition [1]. This method relies on learning an adaptation NN that is capa-ble of transforming input speech features for a certain speaker into a more speaker independent space given a suitable speaker code. Speaker codes are learned for each speaker during adap-tation. The whole multi-speaker training dataset is used to learn the adaptation NN weights. Our previous work has shown that this method is quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However, the proposed method does not work well in the case of convo-lutional neural network (CNN). In this paper, we investigate the fast adaptation of CNN models. We first modify the speaker code based adaptation method to better suit to the CNN struc-ture. Moreover, we investigate a new adaptation scheme using speaker specific adaptive nodes output weights. These weights scale different nodes outputs to optimize the model for new speakers. Experimental results on the TIMIT dataset demon-strates that both methods are quite effective in terms of adapt-ing CNN based acoustic models and we can achieve even better performance by combining these two methods together.
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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 | 0.000 | 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 it