Specifying intonation in a text-to-speech system using only a small dictionary
Bibliographic record
Abstract
In automatic synthesis of speech from English text, the quality of the output speech is highly dependent upon realistic intonation patterns. Most synthesizers have difficulty obtaining sufficient linguistic information from an input text to specify intonation properly. The syntactic structure of the text often specifies where a speaker should pause and which words to stress. However, the problem of parsing natural English is as yet unsolved. The problem is further complicated in systems which may wish to limit memory space and access time by minimizing dictionary size. In most other references, the parsing problem is only mentioned in passing, or parsing occurs on a local basis, ignoring important syntactic structures that encompass the entire sentence. The system described here recognizes function words and uses a set of syntactic constraints to estimate which words are likely to form phrases. This paper is the first to report on parsing details specifically for synthesis, while using only a small dictionary (of about 300 words).
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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 | 0.001 |
| 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".