Capturing Local Variability for Speaker Normalization in Speech Recognition
Bibliographic record
Abstract
The new model reduces the impact of local spectral and temporal variability by estimating a finite set of spectral and temporal warping factors which are applied to speech at the frame level. Optimum warping factors are obtained while decoding in a locally constrained search. The model involves augmenting the states of a standard hidden Markov model (HMM), providing an additional degree of freedom. It is argued in this paper that this represents an efficient and effective method for compensating local variability in speech which may have potential application to a broader array of speech transformations. The technique is presented in the context of existing methods for frequency warping-based speaker normalization for ASR. The new model is evaluated in clean and noisy task domains using subsets of the Aurora 2, the Spanish Speech-Dat-Car, and the TIDIGITS corpora. In addition, some experiments are performed on a Spanish language corpus collected from a population of speakers with a range of speech disorders. It has been found that, under clean or not severely degraded conditions, the new model provides improvements over the standard HMM baseline. It is argued that the framework of local warping is an effective general approach to providing more flexible models of speaker variability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".