Audio Sentiment & Intent Labeling
Annotated hundreds of voice clips to identify sentiment (neutral, positive, negative) and classify user intent (questions, commands, feedback) for training voice AI systems in customer service applications.
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Experienced in labeling prompts and responses for large language models (LLMs), evaluating tone, accuracy, and alignment with intent. Skilled in annotating audio for sentiment, speaker clarity, and intent detection. Also labeled and categorized hundreds of images for object recognition and visual AI training.
Annotated hundreds of voice clips to identify sentiment (neutral, positive, negative) and classify user intent (questions, commands, feedback) for training voice AI systems in customer service applications.
Labeled and categorized images by identifying objects, products, or scenes to train AI models for accurate visual recognition. Ensured consistency and precision to support use cases.
Designed prompts to elicit accurate responses from generative AI, and labeled the resulting outputs for coherence, tone, factual accuracy, and relevance improving model fine-tuning and response quality.
College Degree, Communications
Content Producer
Sports Anchor & Scriptwriter