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CrowdGen
Audio & Voice 2h ago

Native Arabic (Gulf) Speaker for Speech Recording Project

CrowdGen
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Freelance
Not Disclosed
Entry-Level

Job Description

Key Skills Required

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Voice OverAudio Engineering

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The work is straightforward. Read and record 500 short Arabic sentences using the Appen Mobile app. One session takes 1โ€“2 hours.

You're paid when you finish, provided at least 90% of your recordings pass our quality review.

Requirements:

  • Native Arabic (Gulf) speaker in Qatar
  • Age 18 or older
  • Smartphone compatible with the Appen Mobile app
  • Ability to follow recording guidelines

Demographic quotas apply. Task slots depend on region capacity. We'll contact you if slots open later.

If selected, you'll get an email from CrowdGen to set up your account and begin the project.

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CrowdGen is the flagship global contributor platform powered by Appen, an industry-leading data and machine learning company. Operating as a massive, decentralized remote workforce engine, the platform fundamentally democratizes AI development by connecting tech enterprises with over 1 million remote contributors across 200 countries and 500+ languages. Under the hood, CrowdGen powers the intensive data annotation, sentiment analysis, and Reinforcement Learning from Human Feedback (RLHF) required to train the world's most advanced Large Language Models (LLMs) and computer vision systems. Contributors get paid flexibly to complete highly specific micro-tasksโ€”ranging from evaluating search engine accuracy and fact-checking social media content to recording voice samples and testing VR hardware. Their primary target audience includes leading tech giants, AI research labs, and enterprise data science teams that desperately need massive amounts of high-fidelity, human-verified data to remove AI bias and improve model accuracy. What sets CrowdGen apart in the rapidly expanding gig economy is its deep ethical commitment to fair remote compensation and its unparalleled ability to seamlessly deploy a highly diverse, global talent pool to solve incredibly complex machine learning bottlenecks at scale.

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