Hybrid architectures for complex phonetic features classification: a unified approach
Bibliographic record
Abstract
This paper examines how to exploit the advantages of a hybrid approach in order to overcome the drawbacks of classic automatic speech recognition (ASR) systems faced with complex phonetic features. The key idea consists of 'boosting' the capacity of a baseline ASR system to identify features as subtle as emphasis, gemination or relevant vowel lengthening. The 'booster' part is composed of a mixture of time delay neural networks (TDNNs) using an autoregressive version of the backpropagation algorithm. We choose to carry out trials on the Arabic language, which is characterized by the presence of complex features. We use three baseline systems: hidden Markov models (HMM), optimized version of learning vector quantization algorithm (O/sup 2/LVQ1) and classical K-nearest neighbors' classifier (KNN). The reported results showed clearly the effectiveness of the approach since the three hybrid systems (HMM/TDNN, O/sup 2/LVQ1/TDNN, KNN/TDNN) perform significantly better than their corresponding baseline systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".