General AI Data Labeling
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ClassificationGeneral AI Data Labeling I have worked on large-scale AI training datasets entailing text, image, and content annotation for machine learning and natural language processing models. The tasks i have done include text classification, sentiment analysis, tagging, content moderation review, entity recognition, and response quality evaluation. I have also handled chunks of annotation workflows across thousands of data entries while maintaining consistency with the project guidelines and taxonomy standards. worked alongside reviewers and quality assurance teams to resolve edge cases, improve annotation accuracy, and refine labeling instructions. Tasks Performed include: Text categorization and tagging, Sentiment and tone classification, Named entity recognition, AI response evaluation Data validation and correction, Content moderation labeling, Metadata tagging and organization, Project Size, Processed and reviewed large datasets ranging from hundreds to thousands of records daily/weekly basis, Supported AI model training pipelines for conversational AI and NLP systems, Worked within structured annotation platforms and workflow management systems, Quality Measures Adhered To Followed detailed annotation guidelines and decision trees Maintained labeling consistency and inter-annotator agreement standards Conducted self-QA and peer-review checks before submission Met accuracy, turnaround time, and productivity KPIs Escalated ambiguous or edge-case samples for clarification Ensured confidentiality and secure handling of sensitive data Audio & Speech Labeling Performed speech and audio annotation tasks to support automatic speech recognition (ASR) and voice AI model development. Responsibilities included transcription, timestamping, speaker identification, pronunciation review, audio segmentation, and correction of machine-generated transcripts. Worked with multilingual and accented speech samples across various audio qualities and environments to improve speech recognition accuracy and natural language understanding systems. Tasks Performed Audio transcription and correction Speaker diarization and identification Timestamp alignment Accent and pronunciation review Speech intent classification Noise and audio quality tagging Validation of AI-generated transcripts Project Size Processed extensive audio datasets containing short clips, conversations, and long-form recordings Managed large batches of audio files under defined productivity targets Contributed to continuous training and refinement of speech-recognition systems Quality Measures Adhered To Followed strict transcription and formatting standards Maintained high word accuracy and timestamp precision Performed quality assurance reviews on completed annotations Ensured consistency across labeling batches and reviewers Flagged unclear audio or low-confidence segments for escalation Complied with confidentiality and data protection requirements
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