Advanced Audio & Text Annotation for AI Language Models
Annotated thousands of English audio recordings to generate detailed descriptions of human speech attributes including tone, delivery style, pace, intonation, pronunciation patterns, accent markers, and demographic cues. Evaluated AI-generated text outputs using rubric-based scoring systems for factual accuracy, clarity, safety, and adherence to editorial standards. Created and labeled prompt-response datasets for LLM fine-tuning, improving model reasoning, coherence, and alignment with human-like responses. Conducted quality checks and detailed justification reports, ensuring high inter-annotator agreement and dataset reliability. Applied expertise across multiple domains including education, healthcare, policy, and digital communications.