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<p>In this paper, we introduce a novel computer-aided pronunciation training (CAPT) corpus that was designed to facilitate the construction of a high-performance CAPT system and includes the most frequent pronunciation errors made by nonnative Arabic speakers learning Arabic as a second language. This corpus is named the L2AraSpeech corpus and is designed to overcome the lack of nonnative Arabic databases needed to build a high-performance Arabic CAPT system. L2AraSpeech includes speech from 220 speakers from diverse backgrounds in terms of country and mother language. The methodology of building L2AraSpeech consisted of different phases that started with the selection of text that was carefully designed for CAPT systems by linguistic experts, and pronunciation errors were very carefully annotated by expert Arabic linguists. The analysis of pronunciation errors revealed that the most frequent pronunciation errors made by speakers were consistent with linguistic studies in the literature. The corpus was validated in an end-to-end mispronunciation detection and correction (MDD) system, and the results are encouraging.</p>

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Introducing a nonnative Arabic speech corpus for CAPT: L2AraSpeech

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Mohamed A. Bencherif,
  • Mohammed Algabri,
  • Mansour Mohammad Alsulaiman,
  • Ghulam Muhammad,
  • Saad Al-Gahtani,
  • Mohamed A. Mekhtiche,
  • Mohammed Faisal,
  • Fahad Albogamy,
  • Taha Alfakih

摘要

In this paper, we introduce a novel computer-aided pronunciation training (CAPT) corpus that was designed to facilitate the construction of a high-performance CAPT system and includes the most frequent pronunciation errors made by nonnative Arabic speakers learning Arabic as a second language. This corpus is named the L2AraSpeech corpus and is designed to overcome the lack of nonnative Arabic databases needed to build a high-performance Arabic CAPT system. L2AraSpeech includes speech from 220 speakers from diverse backgrounds in terms of country and mother language. The methodology of building L2AraSpeech consisted of different phases that started with the selection of text that was carefully designed for CAPT systems by linguistic experts, and pronunciation errors were very carefully annotated by expert Arabic linguists. The analysis of pronunciation errors revealed that the most frequent pronunciation errors made by speakers were consistent with linguistic studies in the literature. The corpus was validated in an end-to-end mispronunciation detection and correction (MDD) system, and the results are encouraging.