Improved decision trees for phonetic modeling
Bibliographic record
Abstract
Bahl et al. (see ICASSP-91, vol.1, p.185-188, 1991) employed decision trees to specify the acoustic realization of a phone as a function of its context. By using a computationally cheap Poisson-based evaluation function, they were able to account for a much wider context than previous researchers (five preceding and five following phones). We extend this work in four ways. (1) We employ the Poisson criterion to find quickly the M best questions at a node during tree expansion, then use an HMM-based MLE criterion to make the final choice from these (and for pruning trees). (2) Bahl et al. use stopping criteria to halt the growth of a tree, which is then used for speech recognition. It is preferable to grow an over-large tree and then prune it; we apply the efficient GRD expansion-pruning algorithm of Gelfand et al. (see IEEE Trans. PAMI, vol.13, no.2, p.163-174, 1991) to the phonetic modeling problem. (3) Like Bahl et al., we allow questions about a large number of preceding and following phones. However, a given search algorithm may make some of these questions difficult to answer. In addition to the "yes" and "no" children of each question, we grow a "don't know" subtree to be used if a question is unanswerable at present. (4) We have experimented with questions based on phonetic features, as well as questions that ask about the presence of specific phones. Our approach permits an arbitrary feature schema to be read in and used in question generation.
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".