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Record W2801849754 · doi:10.5539/ijel.v8n5p27

Acoustic Characteristics of Pakistani English Vowel Sounds

2018· article· en· W2801849754 on OpenAlexvenueno aff
Abdul Malik Abbasi, Mansoor Ahmed Channa, Masood Akhtar Memon, Stephen John, Kamlesh Kumar

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsVowelFormantDuration (music)Speech recognitionStatistical analysisMid vowelAcousticsSpace (punctuation)MathematicsComputer scienceStatisticsPhysics

Abstract

fetched live from OpenAlex

The purpose of this investigation was to document acoustic characteristics of Pakistani English (PaKE) vowel sounds. The experiment was designed to examine the properties of ten vowels produced by Pakistani ESL learners. The analysis is based on the voice samples of recorded 50 CVC words. Total 5000 (10 10 50=5000) voiced samples were analyzed. The data consisted of 50 words of ten English vowel sounds [i: ɪ e ɔ: æ ə ɑ: u: ɒ ʊ]. Ten ESL speakers recorded their voice samples on Praat speech processing tool installed on laptop. Three parameters were considered i.e., fundamental frequency (F0), vowel quality (F1-F2) and duration. Formant patterns were judged manually by visual inspection on Praat Speech Processing Tool. Analysis of formant frequency shows numerous differences between male and female of F1 and F2, fundamental frequency and duration of English vowels. The voice samples provide evidence for higher and lower frequency of vowel sounds. Additionally, the data analysis illustrates that there were statistical differences in the values of short and long vowels coupled with vowel space plot showing explicit differences in locating the production of vowels of male & female vowel space acoustic realizations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.098
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.098
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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