原田遥

Key Skills

Software

Appen

Top Subject Matter

Japanese transcription

language annotation

Audio segmentation

Top Data Types

Audio

Text

Document

Top Task Types

Transcription

Freelancer Overview

Final Reviewer – Japanese Transcription Tasks (Appen). Brings 11+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen. Education includes High School Diploma, Moriyama High School, Shiga Prefectural (2009). AI-training focus includes data types such as Audio and labeling workflows including Evaluation, Rating, and Transcription.

Labeling Experience

Final Reviewer – Japanese Transcription Tasks (Appen)

AppenAudio

As a final reviewer for Japanese transcription tasks at Appen, I ensured high quality and accuracy of labeled audio data. I applied project-specific rules and conducted precise quality checks to validate completed tasks. My focus was on rigorous grammar checks and notation, contributing to reliable training datasets. • Performed strict final checks of machine-generated transcriptions • Verified and corrected grammar, formatting, and annotation accuracy • Adhered to specialized guidelines for Japanese language data • Supported upstream quality control and reviewer feedback iteration.

As a final reviewer for Japanese transcription tasks at Appen, I ensured high quality and accuracy of labeled audio data. I applied project-specific rules and conducted precise quality checks to validate completed tasks. My focus was on rigorous grammar checks and notation, contributing to reliable training datasets. • Performed strict final checks of machine-generated transcriptions • Verified and corrected grammar, formatting, and annotation accuracy • Adhered to specialized guidelines for Japanese language data • Supported upstream quality control and reviewer feedback iteration.

2026 - Present

Audio Segmentation & Transcription Refiner (Appen)

AppenAudioTranscription

I refined Japanese audio segmentation and transcription in accordance with guideline standards at Appen. This involved correcting audio boundaries and transcription notation to produce high-quality training data. Attention to subtle cues ensured consistency and reliability in the labeled output. • Detected and rectified segmentation inaccuracies in audio data • Edited transcription output for clarity and accuracy • Contributed to production of guideline-compliant training datasets • Enhanced overall model performance through high-fidelity data

I refined Japanese audio segmentation and transcription in accordance with guideline standards at Appen. This involved correcting audio boundaries and transcription notation to produce high-quality training data. Attention to subtle cues ensured consistency and reliability in the labeled output. • Detected and rectified segmentation inaccuracies in audio data • Edited transcription output for clarity and accuracy • Contributed to production of guideline-compliant training datasets • Enhanced overall model performance through high-fidelity data

2026 - 2026

Audio Segmentation & Accent Review Specialist (Appen)

AppenAudio

I reviewed Japanese audio segmentation, transcription, and accent labeling in diverse datasets for Appen. My evaluation emphasized phoneme-level accuracy and adherence to project protocols. I ensured errors were identified and corrected to maintain data quality for AI model training. • Evaluated Japanese audio clips for correct segmentation and transcription • Labeled and reviewed accents with contextual understanding • Improved dataset quality via correction and validation processes • Followed detailed annotation guidelines for phonetic and accent data

I reviewed Japanese audio segmentation, transcription, and accent labeling in diverse datasets for Appen. My evaluation emphasized phoneme-level accuracy and adherence to project protocols. I ensured errors were identified and corrected to maintain data quality for AI model training. • Evaluated Japanese audio clips for correct segmentation and transcription • Labeled and reviewed accents with contextual understanding • Improved dataset quality via correction and validation processes • Followed detailed annotation guidelines for phonetic and accent data

2026 - 2026

Education

Work History