Deep neural network trained with speaker representation for speaker normalization
Bibliographic record
Abstract
A method for speaker normalization in deep neural network (DNN) based discriminative feature estimation for automatic speech recognition (ASR) is presented. This method is applied in the context of a DNN configured for auto-encoder based low dimensional bottleneck (AE-BN) feature extraction where the derived features are used as input to a continuous Gaussian density hidden Markov model (HMM/GMM) based ASR decoder. While AE-BN features are known to provide significant reduction in ASR word error rate (WER) with respect to more conventional spectral magnitude based features, there is no general agreement on how these networks can reduce the impact of speaker variability by incorporating prior knowledge of the speaker. An approach is presented in this paper where spectrum based DNN inputs are augmented with speaker inputs that are derived from separate regression based speaker transformations. It is shown the proposed method could reduce the WER by 3% relative to the best speaker adapted AE-BN CDHMM system.
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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 | 0.000 | 0.000 |
| 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".