USING PRAAT AS A VERIFICATION TOOL FOR ACOUSTIC PECULIARITIES OF FOREIGN PHILOLOGY STUDENTS
Bibliographic record
Abstract
The article summarizes results of a research of acoustic characteristics of English vowel phonemes. To test whether there are any differences between acoustic parameters of short and long English vowels ([ɪ] and [і:]) in articulation of foreigners, we involved 30 students of Applied Linguistics Department, Lesya Ukrainka Volyn National University. All the students are Ukrainians, they speak Ukrainian and study English as a foreign language. To investigate peculiarities of articulation we asked students to use Praat to visualize the sounding of such units with the target vowels ([ɪ] and [і:]): it [ɪt], eat [i:t], eating [ˈi:tɪŋ], ear [ɪə], ease [i:z], easy [ˈi:zi], innocent [ˈɪnəsənt], inner [ˈɪnə], integrity [ɪnˈteɡrəti] and [ɪ]/[i:] in isolation. We managed to trace the manifestation of acoustic features of the phonemes in varied contexts. To verify the experiment results we involved a phonetician who is an English language teacher from Canada. We collected 341 spectrograms, the visual representations of 9 words and 2 sounds in isolation. The criteria, that were used to compare articulation of students who learn English as a foreign language, are the following: 1) pitch; 2) characteristics of formants; 3) median. Comparison of the spectrograms has shown that the difference between articulation of a native speaker and the students constituted 34 Hz, the difference among the students was 5 Hz. Even though we may presume that such a difference is not crucial, it can lead us to an idea that native speakers (in our case this may be done not consciously, since the English speaker is a professional phonetician) pay more attention to their articulation as compared to foreign language learners. The variability can vary depending on a target word, but the average result that takes into account all the spectrograms of all the words pronounced by all the experiment participants incline us to make such a conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".