Accent-robust speech recognition for English in low-resource settings using Manifold Mixup
Bibliographic record
Abstract
We adapt Manifold Mixup theory for accent-robust end-to-end (E2E) Automatic speech recognition (ASR). Accent-variation between a source and target constitutes a domain-mismatch scenario. Manifold Mixup allows cross-domain robustness where a model trained on a source accent generalizes to target accents. We propose a 2-stage training mechanism with manifold mixup using one source accent. Stage 1 is a mixup-enabled cross-entropy based framewise character recognition model. Stage 2 is a Connectionist Temporal Classification (CTC)-loss based E2E ASR model using Stage 1 weights. We show that this model generalizes to unseen accents without any fine-tuning. This is studied for accented English from Indic-TIMIT corpus (6 Indic accents) and Common Voice corpus accent groups UKI (England, Ireland), Oriental (India, Malaysia), NorthAM (USA, Canada), African and ANZ (Australia, New Zealand). This is also studied with another Indian English corpus Svarah, the American English TIMIT corpus and the open audiobook English corpus of Librispeech. The proposed framework, using a Hindi-mixup model, offers absolute gains of around 2% over a non-mixup baseline on unseen test accents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".