Phonetic segmentation for Brazilian Portuguese based on a self-supervised model and forced-alignment
Bibliographic record
Abstract
Um sistema de segmentação fonética para o português brasileiro foi desenvolvido utilizando o modelo Wav2Vec2, uma estrutura de aprendizado auto-supervisionado. O estudo explora a aplicação do Wav2Vec2 na determinação automática de fronteiras fonéticas dentro de sinais de fala. Aproveitando as representações acústicas ricas aprendidas pelo Wav2Vec2, buscamos melhorar a precisão da segmentação fonética. O desempenho do sistema foi comparado com o Montreal Forced Aligner (MFA), demonstrando eficácia notável em várias condições de fala, incluindo vozes neutras e expressivas. Nossa metodologia envolve pré-processamento de transcrições fonéticas, utilização do modelo para alinhamento e pós-processamento para determinar limites fonéticos precisos. Os resultados indicam avanços significativos na detecção de fronteiras fonéticas, especialmente em contextos desafiadores como a fala expressiva.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".