Bibliographic record
Abstract
The Armenian language is an independent branch of the Indo-European language family and the official language of the Republic of Armenia and the Republic of Artsakh. According to various reliable sources, an average of 3 million people in Armenia and 10-12 million people in the Armenian Diaspora use the Armenian language as their native language. The largest communities outside of Armenia are in the United States of America, Canada, the Russian Federation, the Islamic Republic of Iran, the French Republic, the Syrian Arab Republic and the Lebanese Republic. This paper presents the ArmSpeech speech corpus. ArmSpeech is a collection of annotated Armenian speech intended for natural language processing (NLP) technologies research and development. ArmSpeech is designed for speech-to-text and text-to-speech purposes but can be used in other domains also (e.g. language identification). Corpus contains 6206 high-quality audio samples: 11 hours 46 minutes and 26 seconds (11.77 hours) of annotated native Armenian speech of multiple speakers of any age, gender and accent. According to the research results, this is the most extensive Armenian speech corpus in the public domain for speech recognition, speech synthesis and spoken language identification 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".