Greek Audio Segmentation & Transcription

Lifted (Upwork); United States; Remote; USD 15,000 - 30,000; Full time

Job summary

Lifted is seeking Greek transcription experts to support an AI training initiative. You will listen to real audio recordings in Greek and produce precise, verbatim transcripts, including stutters and filler words, while capturing how it is said.

Your work will include applying speaker labels for 2–8 participants, inserting timestamps with 500ms accuracy, and annotating non-speech events such as laughter or background noise. Flexibility to work 20+ hours weekly is encouraged.

Qualifications

• Native Greek fluency with regional accents and informal speech
• Strong listening comprehension and attention to detail
• Ability to produce verbatim transcripts including stutters and filler words
• Experience in transcription, annotation, or linguistics is an advantage
• Comfortable using browser-based tools (training will be provided)
• Reliable internet connection
• Access to a laptop or desktop computer (mobile devices are not supported)
• Reliable availability for 20+ hours per week

Responsibilities

• Listen to Greek audio recordings and produce precise verbatim transcripts
• Apply speaker labels for multi-speaker recordings (2–8 participants)
• Insert timestamps with accuracy within 500 ms
• Annotate non-speech events such as laughter or background noise

Skills

Greek language proficiency

Attention to detail

Verbatim transcription

Reliability / availability

Education

Experience in transcription, annotation, or linguistics

Tools

Browser-based tools

Job description

• Listen to audio recordings and produce accurate, verbatim transcriptions in Greek.
• Capture not only what is said, but how it is said (including filler words, repetitions, and false starts).
• Add speaker labels for multi-speaker recordings (2-8 participants).
• Insert precise timestamps for text segments (within 500ms accuracy).
• Annotate non-speech events such as laughter, background noise, or unclear audio.
• Follow detailed project instructions and maintain high quality standards.
• Maintain steady productivity and reliability throughout the project.

Qualifications

• Native-level fluency in Greek, including familiarity with regional accents and informal speech.
• Strong listening comprehension and attention to detail.
• Ability to produce verbatim transcriptions, including stutters, repetitions, and filler words.
• Experience in transcription, annotation, or linguistics is an advantage.
• Comfortable using browser-based tools (training will be provided).
• Reliable internet connection.
• Access to a laptop or desktop computer (mobile devices are not supported).
• Reliable availability for 20+ hours per week.

Additional information

• Work anytime, provided you meet project deadlines and quality standards.
• Tasks and overall workload may shift based on project needs.
• Consistent availability and productivity are required throughout the assignment.
• Payment will be based on your completed output.

About Lifted

Our enterprise client, a leading provider of high-quality, diverse datasets for AI model development is looking for a Greek Audio Segmentation and Transcription experts to support an AI training initiative aimed at improving Greek speech recognition models.

In this role, you will produce precise, verbatim transcriptions and review audio recordings in Greek. Your contributions will directly help train and enhance next-generation AI speech technologies.

Work Assignment: This project focuses on transcription tasks involving both single-speaker and multi-speaker audio recordings. Tasks may vary in complexity depending on audio quality and number of speakers.

Task Summary (What to Expect)

You will listen to real audio recordings and produce precise, verbatim transcripts - capturing not just what was said, but how it was said. That means every "uh," every repeated word, and every trailing sentence matters. Your work will include:

• Verbatim transcription with a target of 98%+ accuracy
• Timestamping segments with less than 500ms deviation
• Speaker labeling for conversations with 2 to 8 participants
• Event annotation for non-speech elements (e.g., laughter, background noise, unintelligible audio)