Corpus-based discourse analysis of connected speech phenomena in typologically diverse languages
Bibliographic record
Abstract
The paper presents a corpus-driven methodology for discourse analysis of connected speech phenomena in typologically diverse languages (Russian, English, Chinese, Evenki). The study focuses on developing a multilingual speech corpus with a unified annotation system that can be used for comparative analysis of non-canonical phonological patterns across discourse types. The material comprises speech databases including English (news, academic, and regional varieties), Chinese (spontaneous speech, commercial and social advertisement), Evenki (INEL and Amur region corpora), and Russian (educational discourse). Within corpus-driven approach, the following methods and tools were used: automatic alignment tools (Montreal Forced Aligner, BAS WebServices), manual expert validation, file format conversion (XML, EXMARaLDA), and Python scripting (for data processing). As a result, a standardized corpus annotation system has been developed to compare natural phonetic modifications across languages. The research demonstrated the effectiveness of automated processing tools at the same time emphasizing the necessity of manual expert correction. Query templates for the EXAKT corpus manager have been designed to investigate modification frequency and contextual patterns. Future research directions include corpus expansion, development of machine learning algorithms for automatic modification detection.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".