Author: Kseniia Briling
This document examines quality assurance failure modes in audio annotation workflows, with examples from Russian-language speech projects.
- underspecified annotation guidelines
- threshold-based criteria without calibration anchors
- taxonomy design failures in classification systems
- reviewer feedback loops that unintentionally simplify datasets
In large-scale annotation projects, contributors often adapt to the evaluation system rather than to the real-world task. Over time, this can create datasets that are easier to review but less representative of natural user behaviour. The document analyses the structural mechanisms behind this process and proposes practical remediation strategies for:
- guideline authors
- QA leads
- annotation project managers
- dataset operations teams
audio-annotation-qa/audio-annotation-problems.pdf— full paper
audio annotation • linguistic QA • dataset quality • reviewer calibration • guideline design • taxonomy design • Russian language • NLP data quality