Justin Giraldo

Key Skills

Software

Appen

Labelbox

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Audio Recording

Classification

Object Detection

Prompt Response Writing SFT

Translation Localization

Freelancer Overview

As a bilingual data labeling specialist, I have experience working on advanced AI training projects for leading platforms like Outlier AI and Aligner. My expertise centers on audio data annotation, including voice activity detection or voice recording prompts and conversations. where precision and attention to detail are essential. I have also contributed to prompt response evaluations, ensuring generated outputs meet high standards. My skills include detailed labeling of diverse data types, expertly annotating a wide range of audio data types—such as voice recordings, speech-to-text samples, and emotional speech datasets—with exceptional accuracy, I am confident in collaborating on challenging projects that require both linguistic and technical strengths

Labeling Experience

Voice Activity Detection

LabelboxAudioAudio Recording

The Voice Activity Detection (VAD) project involves labeling speech and non-speech segments in audio with precise token annotations, focusing on user and assistant interactions. It is a moderate-sized project requiring about 2 hours per data row, with strict quality controls to ensure accurate timestamps, token types, and continuous speech labeling for training speech recognition models.

The Voice Activity Detection (VAD) project involves labeling speech and non-speech segments in audio with precise token annotations, focusing on user and assistant interactions. It is a moderate-sized project requiring about 2 hours per data row, with strict quality controls to ensure accurate timestamps, token types, and continuous speech labeling for training speech recognition models.

2025 - 2025

Blueberry Bagels V2

Scale AIAudioPrompt Response Writing SFTAudio Recording

The project involved annotating large volumes of conversational and podcast audio data to train machine learning models; tasks included applying specific labels for engagement, tone, and humor, with strict quality guidelines and review processes to ensure accuracy and consistency throughout the dataset

The project involved annotating large volumes of conversational and podcast audio data to train machine learning models; tasks included applying specific labels for engagement, tone, and humor, with strict quality guidelines and review processes to ensure accuracy and consistency throughout the dataset

2025 - 2025

Multispeaker Convo Audio

Scale AIAudioAudio Recording

The Multispeaker Convo Audio project records natural conversations with 3 to 5 participants, producing separate and combined audio files. It focuses on clear, natural multi-voice interactions with proper topic and language categorization, maintaining quality through clear audio and adherence to guidelines

The Multispeaker Convo Audio project records natural conversations with 3 to 5 participants, producing separate and combined audio files. It focuses on clear, natural multi-voice interactions with proper topic and language categorization, maintaining quality through clear audio and adherence to guidelines

2025 - 2025

Xylophone Conver

Scale AIAudioAudio Recording

The project involves recording natural conversations, producing three audio files per session, and categorizing them by language and topic. It aims for high-quality, engaging interactions, with a focus on clarity and natural flow, across sessions ranging from 10 to 20 minutes. Quality is ensured through fluency, minimal noise, and adherence to guidelines.

The project involves recording natural conversations, producing three audio files per session, and categorizing them by language and topic. It aims for high-quality, engaging interactions, with a focus on clarity and natural flow, across sessions ranging from 10 to 20 minutes. Quality is ensured through fluency, minimal noise, and adherence to guidelines.

2025 - 2025

Xylophone Espectrum

Scale AIAudioAudio Recording

The Xylophone Spectrum project involves recording and transcribing natural English speech, labeling conversation types and environment details, and ensuring data quality to train AI speech models effectively.

The Xylophone Spectrum project involves recording and transcribing natural English speech, labeling conversation types and environment details, and ensuring data quality to train AI speech models effectively.

2025 - 2025

Show more

Education

Work History