The Experimental-Phonetic Analyses of the Discourse Intonation in the English and Azerbaijan Languages
Bibliographic record
Abstract
The article deals with the experimental-phonetic analyses of the discourse intonation in the English and Azerbaijan languages. Having researched the article, it becomes clear that discourse intonation (DI) is an approach to the teaching and analysis of everyday speech. The characteristics of intonation components in the formation of discourse have been touched upon in the article. The intonation is mentioned to be one of the main means in the formation of the discourse. It is a known fact that speech styles can be characterized by their lexical, syntactic and phonetic features. The attention is drawn to the distinguishing points of the speech of the people having various professions such as the speech styles of a teacher and a driver should be different not only from the lexical point of view but also from the phonetic point of view. During the conversation, one can come across some nuances of the speaker’s intellectual level, life experience and social status. It is also important to remember that the subject of the conversation is meant to be an important factor too. The object of the conversation ensures the stylistic formation of the idea. The importance of the experiment has been taken into a special consideration in the article as well. The opinion of academician L. V. Sherba that stresses the importance of the experiment has been analyzed by the author. The factors that are needed to be followed by while carrying out the language facts have been fulfilled in the article. The author tries to prove that DI is concerned with the speakers’ moment-by-moment context-referenced choices. It recognizes four systems of speaker’s choice: prominence, tone, melodicy, and termination. The discourse samples having been chosen for the experiment are fulfilled by using various sentence types. Besides, the inside structure of the sentences and their lexical contents are also taken into account in the article. Some discourse samples have been chosen in the comparable languages to be experimented in order to distinguish the intonation nature of the discourse. The experiment has been carried out by using the program “Praat”. It is noteworthy to mention that the program “Praat” is known to be a computer operation used to analyze speech sounds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".