Quality Control Frutulip

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Top Label Types

Text Generation

Action Recognition

Audio Recording

Freelancer Overview

I am a results-driven data labeling and AI training data specialist with over 7 years of hands-on experience in annotating, validating, and quality-assuring large-scale datasets for NLP, voice recognition, and conversational AI projects. My expertise spans RLHF tasks, multilingual content classification, NER, sentiment and intent labeling, and audio transcription, with a proven track record of delivering 98% data acceptance rates on high-profile projects at Appen and Fortune 500 clients. As a native French and fluent English speaker with professional proficiency in Arabic, I bring rare trilingual coverage to AI model development, ensuring accurate localization and cultural relevance across global markets. I am adept at following complex annotation guidelines, performing rigorous QA testing, and providing actionable feedback for AI-powered applications, making me a valuable asset for any team focused on high-quality AI training data.

Labeling Experience

Speech & Audio Data Annotator — Voice Recognition QA

AppenAudioText GenerationAction Recognition

Annotated and quality-reviewed audio datasets for AI speech recognition systems. Tasks included transcribing spoken audio in French, English, and Arabic; tagging speech patterns, accents, and disfluencies; labeling audio segments by speaker, noise level, and content type; and evaluating transcription accuracy against ground truth data. Contributed to improving model performance on multilingual voice recognition benchmarks through detailed audio quality evaluation and structured defect reporting. Languages Used: French, English, Arabic Task Types: Audio transcription, speech tagging, voice recognition QA, multilingual audio annotation

Annotated and quality-reviewed audio datasets for AI speech recognition systems. Tasks included transcribing spoken audio in French, English, and Arabic; tagging speech patterns, accents, and disfluencies; labeling audio segments by speaker, noise level, and content type; and evaluating transcription accuracy against ground truth data. Contributed to improving model performance on multilingual voice recognition benchmarks through detailed audio quality evaluation and structured defect reporting. Languages Used: French, English, Arabic Task Types: Audio transcription, speech tagging, voice recognition QA, multilingual audio annotation

2025 - 2025

Education

Work History