Smith Annette

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Top Label Types

Emotion Recognition

Action Recognition

Tracking

Prompt Response Writing SFT

Audio Recording

Freelancer Overview

I am a computer science–trained mathematician and software engineer with over 10 years of experience specializing in data annotation, AI training data quality, and correctness-critical labeling. My background spans formal proof engineering, mathematical reasoning, and human-in-the-loop AI systems, where I have delivered high-precision annotations, structured explanations, and rigorous QA for advanced AI models. I am highly skilled in tools such as Lean (Lean 4, Lean 3), Python, SQL, and Excel, and have worked extensively on projects involving mathematical reasoning, multilingual data (English, Spanish, German), and technical dataset creation. My experience includes supporting AI research, optimizing prompt engineering, and ensuring dataset consistency and accuracy, all within remote and asynchronous environments that demand strict quality standards. I am passionate about building reliable, interpretable datasets that advance the capabilities of AI systems.

Labeling Experience

Audio annotation for speech recognition and emotion detection

Scale AIAudioEmotion RecognitionAction Recognition

Performed audio data annotation for speech recognition and emotion detection models by labeling and classifying voice recordings according to predefined guidelines. Tasks included emotion recognition, relationship labeling between audio segments, and classification of speech patterns to support training of machine learning models. Reviewed recordings for clarity and accuracy, ensured consistent labeling across datasets, and contributed to improving the performance of AI systems used for speech understanding and audio-based emotion analysis.

Performed audio data annotation for speech recognition and emotion detection models by labeling and classifying voice recordings according to predefined guidelines. Tasks included emotion recognition, relationship labeling between audio segments, and classification of speech patterns to support training of machine learning models. Reviewed recordings for clarity and accuracy, ensured consistent labeling across datasets, and contributed to improving the performance of AI systems used for speech understanding and audio-based emotion analysis.

2022 - 2023

Education

Work History