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Juri Iorio

Juri Iorio

AI Trainer / Evaluator - Multimodal AI & Audio Processing

ITALY flag
Melito di Napoli, Italy
$19.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
ImageImage
TextText
VideoVideo

Top Label Types

Evaluation Rating

Freelancer Overview

I am an experienced AI Trainer and Data Evaluator with a strong background in data annotation, labeling, and model output assessment, particularly in multimodal AI tasks such as synthetic speech evaluation and artifact detection. My work at RWS Group has equipped me with the skills to design evaluation rubrics, ensure data quality through rigorous QA checks, and provide detailed technical feedback to enhance AI model performance. With over seven years in sound design and audio processing, I excel at critical listening and spectral analysis, which directly supports my ability to detect errors in both audio and text datasets. I am also proficient in English-Italian technical translation, ensuring semantic accuracy in multilingual data. I thrive in remote, fast-paced environments and am committed to maintaining high standards of accuracy, guideline adherence, and timely delivery in every project.

IntermediateEnglishItalian

Labeling Experience

Ai Trainer Evaluator and Annotator

OtherAudioEvaluation Rating
Subjective evaluation of AI-generated Italian dubbing audio for AI model development. Dubbing quality assessment based on multiple criteria: Instability and meaningfulness Voice naturalness Prosody and intonation Voice similarity (similarity of the voice to the original/target) Audio quality degradation Text-to-speech similarity (alignment/synchronization between the script text and the spoken audio) Used the proprietary SRT Halo platform. Followed competitive benchmarking and monolingual subjective evaluation guidelines. Average execution time ~480 seconds. Contributed to the improvement of AI-based dubbing systems through detailed human evaluations.

Subjective evaluation of AI-generated Italian dubbing audio for AI model development. Dubbing quality assessment based on multiple criteria: Instability and meaningfulness Voice naturalness Prosody and intonation Voice similarity (similarity of the voice to the original/target) Audio quality degradation Text-to-speech similarity (alignment/synchronization between the script text and the spoken audio) Used the proprietary SRT Halo platform. Followed competitive benchmarking and monolingual subjective evaluation guidelines. Average execution time ~480 seconds. Contributed to the improvement of AI-based dubbing systems through detailed human evaluations.

2025

Education

N

N/A

High School Diploma, General Studies

High School Diploma
2018 - 2023

Work History

F

Freelance Sound Design & Audio Engineering

Sound Designer & Audio Processing Specialist

Naples
2019 - Present