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Naim Terbeh

Naim Terbeh has 2 papers in our library, in 2012–2016, most of them on Semitic languages, Speech & spoken language; often with Mounir Zrigui.

2 papers in our library · 2012–2016

OpenAlex · ORCID · h-index 6 · 19 works · 122 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #9951

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of MonastirMonastir · Tunisia2013–2022 · 14 paperscurrent🎓University of SfaxSfax · Tunisia2015–2016 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 10The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksEducation · 1 workSocial Sciences · 1 workExperimental and Cognitive Psychology · 1 workMedicine · 1 workPhysiology · 1 workPsychology · 1 work

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2 worksSpeech & spoken language · 1 work

 Works by year

2010s

20161 paper20121 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech (2016 · 6 citations)
    The paper targets Arabic speech assessment. It decides whether a continuous utterance is healthy or pathological, and for pathological speech it finds which phonemes the speaker cannot produce and what replaces them. The intended users are native speakers with language disabilities and learners of Arabic.
  2. Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition (2012 · 4 citations)
    The authors build a pronunciation-training tool for learners of Arabic. Most earlier computer-assisted language learning (CALL) systems handled only written language. This one listens to the learner. It is built on an HMM-based speech recognizer made with CMU Sphinx3, and the authors chose HMMs partly because they…

 Co-authors here

1 co-author with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Updated

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