Text-to-Speech Conversation Evaluator

Text-to-Speech Conversation Evaluator is a remote evaluation track for reviewing text to speech conversation evaluation prompts and responses against Aura One's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

AI data reviewers help turn text to speech conversation evaluation outputs into auditable labels, rationales, and regression cases for Aura One Human Data.

• Evaluate text to speech conversation evaluation model outputs against a versioned rubric and assign severity tags for Text-to-Speech Conversation Evaluator assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.
• Document recurring failure modes so the modeling team can target them in the next training run.

• Prior evaluation, annotation, or human-rater experience on text to speech conversation evaluation or adjacent content for Text-to-Speech Conversation Evaluator work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least