Emily Peters

Key Skills

Software

CVAT

Labelbox

Prodigy

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Polygon

Segmentation

Object Detection

Tracking

Emotion Recognition

Audio Recording

Transcription

Freelancer Overview

I am a detail-oriented AI Training Specialist with over three years of hands-on experience in data annotation and labeling for computer vision and machine learning projects. My expertise spans image, video, and audio datasets, where I have utilized tools like Labelbox, CVAT, Supervisely, and YOLO to perform bounding box annotation, polygon segmentation, semantic segmentation, and multi-object tracking. I have worked closely with data scientists and ML engineers to refine labeling guidelines and ensure high-quality datasets, consistently maintaining annotation accuracy above 98%. My background includes supporting autonomous vision systems, speech recognition, and sound classification tasks, as well as developing QA processes to optimize dataset performance. I am passionate about delivering precise, reliable training data to enhance AI model accuracy and efficiency.

Labeling Experience

Audio Transcription & Emotion Annotation for Speech Recognition Models

LabelboxAudioEmotion RecognitionAudio Recording

Participated in a large-scale audio annotation project focused on improving automatic speech recognition (ASR) and conversational AI systems. The project involved transcribing and labeling over 15,000 hours of multilingual audio recordings, including customer service calls, virtual assistant interactions, and spontaneous conversational speech. Responsibilities included verbatim transcription with speaker diarization, timestamp alignment, noise identification, and sound event classification. I also annotated emotional tone categories such as neutral, positive, negative, frustrated, and excited to support emotion recognition model training. Special attention was given to handling background noise, overlapping speech, regional accents, and low-audio clarity recordings to ensure dataset consistency. Strict quality assurance processes were followed, including double-pass review systems, random batch audits, word error rate (WER) checks, and compliance with standardized transcription.

Participated in a large-scale audio annotation project focused on improving automatic speech recognition (ASR) and conversational AI systems. The project involved transcribing and labeling over 15,000 hours of multilingual audio recordings, including customer service calls, virtual assistant interactions, and spontaneous conversational speech. Responsibilities included verbatim transcription with speaker diarization, timestamp alignment, noise identification, and sound event classification. I also annotated emotional tone categories such as neutral, positive, negative, frustrated, and excited to support emotion recognition model training. Special attention was given to handling background noise, overlapping speech, regional accents, and low-audio clarity recordings to ensure dataset consistency. Strict quality assurance processes were followed, including double-pass review systems, random batch audits, word error rate (WER) checks, and compliance with standardized transcription.

2024 - 2025

Advanced Video Annotation & Multi-Object Tracking for Smart Surveillance Systems

LabelboxVideoBounding BoxPolygon

Contributed to a large-scale video annotation project designed to train computer vision models for smart surveillance and behavior analysis systems. The project involved labeling over 8,000 hours of high-definition surveillance footage captured in indoor and outdoor environments. Responsibilities included drawing bounding boxes around moving objects such as pedestrians, vehicles, and bicycles, and performing frame-by-frame multi-object tracking to maintain consistent object IDs across video sequences. I also labeled human actions including walking, running, loitering, entering/exiting restricted areas, and abnormal behavior detection. Special attention was given to occlusion handling, motion blur, camera angle variation, and crowded scenes to ensure temporal consistency and model reliability. Using CVAT and Supervisely, I maintained structured annotation workflows and applied YOLO-based validation techniques to test object detection alignment. Strict quality control processes were foll

Contributed to a large-scale video annotation project designed to train computer vision models for smart surveillance and behavior analysis systems. The project involved labeling over 8,000 hours of high-definition surveillance footage captured in indoor and outdoor environments. Responsibilities included drawing bounding boxes around moving objects such as pedestrians, vehicles, and bicycles, and performing frame-by-frame multi-object tracking to maintain consistent object IDs across video sequences. I also labeled human actions including walking, running, loitering, entering/exiting restricted areas, and abnormal behavior detection. Special attention was given to occlusion handling, motion blur, camera angle variation, and crowded scenes to ensure temporal consistency and model reliability. Using CVAT and Supervisely, I maintained structured annotation workflows and applied YOLO-based validation techniques to test object detection alignment. Strict quality control processes were foll

2023 - 2024

Advanced Image Annotation for Autonomous Vehicle Object Detection

CVATImageBounding BoxPolygon

Worked on a large-scale image annotation project supporting object detection models for autonomous vehicle systems. The project involved labeling over 120,000 high-resolution street-view images containing vehicles, pedestrians, cyclists, traffic signs, traffic lights, and lane markings. I performed precise bounding box annotations for dynamic and static objects, as well as polygon and semantic segmentation for complex elements such as drivable areas and road lanes. Special attention was given to occlusion cases, small-object detection, low-light conditions, and weather-related distortions to ensure high dataset reliability. I used CVAT and Labelbox for annotation and applied YOLO-based validation workflows to test dataset consistency and model alignment. Strict quality control procedures were followed, including multi-level peer reviews, inter-annotator agreement checks, and random sampling audits of completed batches. Through consistent adherence to guidelines and detailed validation

Worked on a large-scale image annotation project supporting object detection models for autonomous vehicle systems. The project involved labeling over 120,000 high-resolution street-view images containing vehicles, pedestrians, cyclists, traffic signs, traffic lights, and lane markings. I performed precise bounding box annotations for dynamic and static objects, as well as polygon and semantic segmentation for complex elements such as drivable areas and road lanes. Special attention was given to occlusion cases, small-object detection, low-light conditions, and weather-related distortions to ensure high dataset reliability. I used CVAT and Labelbox for annotation and applied YOLO-based validation workflows to test dataset consistency and model alignment. Strict quality control procedures were followed, including multi-level peer reviews, inter-annotator agreement checks, and random sampling audits of completed batches. Through consistent adherence to guidelines and detailed validation

2023 - 2023

Education

Work History