Data Annotation
Data Recording – Delivering clean, natural, and versatile voice recordings across multiple tones and contexts. Segmentation & Transcription – Marking audio boundaries, transcribing spoken content, and aligning text with speech. Annotation – Labeling intonation, pauses, emotions, and speaker details to support prosody modeling. Quality Review – Ensuring audio clarity, correct pronunciation, and dataset consistency. The project size involved thousands of audio samples across different domains, with strict guidelines and turnaround times. Quality measures included adherence to linguistic accuracy, rejection rate thresholds below 2%, and multiple review cycles to guarantee reliable data outputs. By maintaining consistency, precision, and cultural relevance, the project successfully contributed to the development of inclusive, human-like AI voices.