Model‐based speaker normalization methods for speech recognition
Bibliographic record
Abstract
Abstract A speaker normalization method using a speech generation model is proposed in order to achieve high‐performance speaker adaptation with a small amount of adaptation data. The speaker‐ and phoneme‐dependent vocal tract area function is approximated by the corresponding area function produced by the articulatory model of a standard speaker, combined with phoneme‐independent feature quantities of the vocal‐tract shape of the normalized target speaker as estimated from the formant frequencies of two vowels. The frequency warping functions are determined from the formant frequencies of speech calculated from the vocal‐tract area functions thus obtained, and normalization of the uttered speech is performed by stretching the speech spectrum in the frequency‐axis direction. Continuous phoneme recognition experiments using phoneme connection rules show that the recognition error using a gender‐dependent model is reduced by about 30% in the proposed method and that recognition performance superior to that of vocal‐tract length normalization is obtained. The recognition performance of the proposed method is also equivalent to that of speaker adaptation by moving vector field smoothing (VFS) using 10 phonetically balanced sentences, showing that high‐performance speaker adaptation using a small amount of adaptation data can be achieved by the proposed method. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(2): 45–56, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/ecjb.10119
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".