Speaker adaptive bottleneck features extraction for LVCSR based on discriminative learning of speaker codes
Bibliographic record
Abstract
Recently, several fast speaker adaptation methods based on the so-called speaker codes (SC) have been proposed for the hybrid DNN-HMM speech recognition model [1, 2, 3]. In these methods the target speaker features are modified to match the given speaker-independent models or the speaker-independent models are transformed towards one particular speaker based on the discriminative learning of speaker codes. Previous researches have shown that these proposed SC-based adaptation methods are very effective to adapt large DNN models using only a small amount of adaptation data. In this work, we have explored the combination of direct speaker adaptation technique in model space based on speaker codes (mSA-SC) and bottleneck features where mSA-SC is used as an extraction instrument of speaker adaptive bottleneck features. We have evaluated the proposed speaker adaptive bottleneck features extraction method in two speech recognition tasks, namely PSC Mandarin task and large scale 320-hr Switchboard task. Experimental results have verified that it is quite suitable for very large scale tasks. For example, the Switchboard results have shown that it can achieve relative 9% reduction in word error rate on an unsupervised speaker adaptation scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".