A speech synthesizer for Persian text using a neural network with a smooth ergodic HMM
Why this work is in the frame
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Bibliographic record
Abstract
The feasibility of converting text into speech using an inexpensive computer with minimal memory is of great interest. Speech synthesizers have been developed for many popular languages (e.g., English, Chinese, Spanish, French, etc.), but designing a speech synthesizer for a language is largely dependant on the language structure. In this article, we develop a Persian synthesizer that includes an innovative text analyzer module. In the synthesizer, the text is segmented into words and after preprocessing, a neural network is passed over each word. In addition to preprocessing, a new model (SEHMM) is used as a postprocessor to compensate for errors generated by the neural network. The performance of the proposed model is verified and the intelligibility of the synthetic speech is assessed via listening tests.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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 it