Add Data Labeling Experience Showcase a specific data labeling/annotation project by adding it to your OpenTrain Profile. Detail your expertise and attract potential clients. Title * Enter a Title for this entry Data Type * Select data type Labeling Type * Bounding BoxPolygonPoint/Key PointPolylineEntity (NER) ClassificationSegmentationCuboidRelationshipClassificationQuestion AnsweringText GenerationEmotion RecognitionObject DetectionAction RecognitionTrackingText SummarizationDiagnosisTranslation/LocalizationGeocodingRoutingMappingLand Cover ClassificationRLHFFine-tuningEvaluation/RatingRed TeamingComputer Programming/CodingData CollectionFunction CallingPrompt + Response Writing (SFT)Audio RecordingTranscription Labeling Software Used * e.g. CVAT, Labelbox, Scale AI, Appen, etc. Subject Matter/Industry * e.g. Autonomous Vehicles, LLM, Healthcare... Start Date * Month Year End Date * Month Year On-going Project Project Description * Describe the scope of the project, the specific data labeling tasks performed, project size, and the quality measures adhered to. Additional Information (optional) Any other details you would like to share about this data labeling/annotation project. Save
LabelboxAudioEvaluation RatingData Collection
Worked on large-scale audio transcription and data labeling for AI training, focused on aviation and ATC-style communications.
Tasks included accurately transcribing audio data, following strict project guidelines, handling corrupted or overlapping audio using skip criteria, and applying brackets where speech was uncertain instead of guessing.
Quality control was prioritized by reviewing each submission for accuracy, formatting, and guideline compliance.
The project emphasized high annotation standards, aggressive skipping to protect data quality, and continuous improvement based on reviewer feedback.
Worked on large-scale audio transcription and data labeling for AI training, focused on aviation and ATC-style communications. Tasks included accurately transcribing audio data, following strict project guidelines, handling corrupted or overlapping audio using skip criteria, and applying brackets where speech was uncertain instead of guessing. Quality control was prioritized by reviewing each submission for accuracy, formatting, and guideline compliance. The project emphasized high annotation standards, aggressive skipping to protect data quality, and continuous improvement based on reviewer feedback.
2023 - 2025