Transcription Specialist – Perkiomen-D Project (EN-US)

Remote Full-time
Contribute to the development of innovative AI speech recognition technology at CrowdGen. As a freelance Transcription Specialist for Project Perkiomen-D, your task is to produce highly accurate transcriptions of speech combined with annotation mark-up. We will provide you with Guidelines for how to transcribe and annotate the audio. The data will be used to develop better AI-powered speech recognition for US English., helping to create inclusive and unbiased AI models that break down language barriers.

Key Responsibilities:

• Accurate Transcription: Produce high-quality transcriptions of spoken English audio, adhering to specific tagging conventions and annotation guidelines.

• Data Annotation: Enhance the transcribed data by adding relevant annotations, ensuring that the AI models can learn effectively.

• Quality Assurance: Deliver work that meets the highest standards of accuracy and consistency, supporting the development of advanced AI speech recognition models.

Requirements:

• Must reside in the United States and have a strong command of English.

• Strong attention to detail and excellent listening skills

• Understanding of basic linguistic concepts and US English spelling conventions

• Prior experience in transcription or a related field

• Reliable internet connection

Compensation:

Your earnings will be based on the number of completed transcriptions. The payrate per item can be found in your project list.

This role is a project-based opportunity with CrowdGen, where you will join the CrowdGen Community as an Independent Contractor. If selected, you will receive an email from CrowdGen regarding the creation of an account using your application email address. You will need to log in to this account and reset the password, complete the setup requirements, and proceed with your application for this project-based role.

By joining this project, you will contribute to the advancement of responsible AI technology, ensuring that speech recognition systems are accurate, inclusive, and free from bias.

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