{
  "abstract": "This study proposes an AI-integrated computational framework for pronunciation error analysis through native–learner speech comparison. The framework integrates Whisper-based Automatic Speech Recognition (ASR), acoustic feature extraction, statistical analysis, and machine learning to analyze learner pronunciation. Unlike previous studies that primarily rely on individual transcription-based or acoustic measures, the proposed framework integrates multiple pronunciation indicators into a composite Pronunciation Accuracy Score (PAS) formulated in this study. The framework was evaluated using a dataset comprising 45 non-native English learners and one native English speaker. PAS combines Pronunciation Error Rate (PER), Word Error Rate (WER), Pitch Similarity, Rhythm Similarity, and Mel-Frequency Cepstral Coefficient (MFCC) Similarity. Pearson correlation analysis showed that Rhythm Similarity (r = 0.544) and Pitch Similarity (r = 0.510) exhibited the strongest positive correlations with PAS, whereas Word Error Rate showed the strongest negative correlation (r = −0.495). K-Means clustering identified three learner groups with distinct pronunciation characteristics within the tested dataset. Although evaluated on a relatively small dataset without validation against expert human ratings, the proposed framework provides a promising foundation for Computer-Assisted Pronunciation Training (CAPT) and future multilingual pronunciation assessment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Hastha Sunardi"
    },
    {
      "name": "Rendra Gustriansyah"
    },
    {
      "name": "Fery Antony"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f396vvt4dt15f.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1812",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1812",
  "id": "f396vvt4dt15f",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1812",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1702|Speech Recognition and Synthesis"
    ]
  ],
  "openalex": "W7223457433",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1812/1074",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/f396vvt4dt15f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:45Z",
      "native_id": "77d31ac6cdeb4dec861d6f93b64d632b",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1812"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "AI-Integrated Computational Framework for Pronunciation Error Analysis Through Native–Learner Speech Comparison",
  "title_text": "AI-Integrated Computational Framework for Pronunciation Error Analysis Through Native–Learner Speech Comparison",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:45Z",
  "url": "https://research.draconitic.com/paper/f396vvt4dt15f",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}