Automatic accentedness rating using deep neural networks
Bibliographic record
Abstract
Automatic accentedness rating has the potential to improve many human-computer interactions involving speech, including the adaptation of automatic speech recognition or other artificial intelligence models to the speaker’s accent. Accent ratings may also be used as a metric by which language learners can quantify their progress. This study employs bidirectional long short-term memory layers in a neural network to predict human ratings of the accentedness of recorded speech. Speech data are extracted in five-second segments from over 2,000 first- and second-language English speakers from multiple corpora. Human ratings are obtained in an online experiment where participants rate the accentedness of a given speech recording on a 9-point Likert scale. Mel-frequency cepstral coefficients and mel-filterbank energy features are tested as speech input representations for the neural network. When models are evaluated on a held out test set, the model’s predictions and average human ratings are correlated (r=0.57). While previous methods which automatically compare speech that has been transcribed or use accent-specific Gaussian mixture models to compare acoustic templates perform better, the present model requires no transcription or template and can perform accent-general inference.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".