Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
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Bibliographic record
Abstract
The performance of automatic speech recognition (ASR) may be degraded when accented speech is recognized because the speech has some linguistic differences from standard speech. Conventional accented speech recognition studies have utilized the accent embedding method, in which the accent embedding features are directly fed into the ASR network. Although the method improves the performance of accented speech recognition, it has some restrictions, such as increasing the computational costs. This study proposes an efficient method of training the ASR model for accented speech in a domain adversarial way based on the Domain Adversarial Neural Network (DANN). The DANN plays a role as a domain adaptation in which the training data and test data have different distributions. Thus, our approach is expected to construct a reliable ASR model for accented speech by reducing the distribution differences between accented speech and standard speech. DANN has three sub-networks: the feature extractor, the domain classifier, and the label predictor. To adjust the DANN for accented speech recognition, we constructed these three sub-networks independently, considering the characteristics of accented speech. In particular, we used an end-to-end framework based on Connectionist Temporal Classification (CTC) to develop the label predictor, a very important module that directly affects ASR results. To verify the efficiency of the proposed approach, we conducted several experiments of accented speech recognition for four English accents including Australian, Canadian, British (England), and Indian accents. The experimental results showed that the proposed DANN-based model outperformed the baseline model for all accents, indicating that the end-to-end domain adversarial training effectively reduced the distribution differences between accented speech and standard speech.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it