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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Ar-DAD: Arabic diversified audio dataset.

Mohammed Lataifeh1, Ashraf Elnagar1

  • 1Department of Computer Science, University of Sharjah, Sharjah 27272, United Arab Emirates.

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|December 9, 2020
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Summary
This summary is machine-generated.

Researchers developed a large Arabic audio dataset for speaker identification. This dataset features 15,810 clips from 30 Quran reciters, aiding diverse applications.

Keywords:
Arabic audio clipsCantillationsDeep learningImitatorsMachine learningQuran recitationsSpeaker identification

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Area of Science:

  • Speech processing
  • Digital signal processing
  • Linguistics

Background:

  • Automatic speaker identification and verification are crucial in various fields.
  • A significant gap exists in classified, multi-purpose Arabic audio datasets.
  • Existing datasets lack the scale and diversity for advanced research.

Purpose of the Study:

  • To introduce a comprehensive Arabic audio dataset for speaker recognition research.
  • To address the scarcity of categorized Arabic audio resources.
  • To facilitate research in speaker identification and verification using Quranic recitations.

Main Methods:

  • Compilation of 15,810 audio clips from 30 popular Quran reciters.
  • Inclusion of 37 chapters from the Holy Quran, organized by verse.
  • Addition of 397 clips from 12 imitators for comparative analysis.

Main Results:

  • Creation of a large-scale, diverse Arabic audio dataset.
  • Structured organization of audio clips by reciter, chapter, and verse.
  • Dataset enables cross-comparison of text, reciters, and authenticity.

Conclusions:

  • The dataset's volume and diversity support wide-ranging speaker identification applications.
  • It establishes a new benchmark for structuring large-scale audio datasets.
  • This resource is expected to advance research in Arabic speaker recognition.