Accurate Data Labeling for Voice Security: Reality Defender's Success Story

The Importance of Reliable Data in Voice Technology

For Reality Defender, a multi-model and multimodal deepfake detection platform, ensuring voice security meant starting with high-quality labeled data. By partnering with our team at Sapien, Reality Defender overcame technical challenges and created a strong foundation for their R&D efforts. Tasked with handling medium-sized datasets (1,001 - 10,000 data points), Sapien helped the team structure valuable data with strict adherence to data formatting and consistency standards.

Challenge: Managing Complexity and Ensuring Compatibility

Reality Defender needed precise data labeling services to meet the demands of their in-house frameworks. Their lack of standardized data interfaces presented significant integration hurdles, complicating their workflow and increasing the risk of delays.

Solution: A Collaborative and Iterative Approach

We started by thoroughly reviewing Reality Defender's project requirements, including data typing and formatting challenges. We ensured the labeled data aligned with their existing frameworks, such as Pydantic and Serde. This collaboration was key to developing a clear and effective plan to address these issues.

Initially, we shared sample datasets to validate the structure and compatibility with their systems. Based on the feedback received, we refined our labeling processes to address any inconsistencies or misalignments. By adopting a phased approach, we allowed for continuous improvement and validation at every stage of the project.

Our team adjusted the outputs iteratively, ensuring they met Reality Defender’s technical requirements for voice-based AI detection. Each audio clip, ranging from 1 to 8 seconds, was carefully analyzed by identifying whether it sounded “odd” or “not odd,” marking the precise timestamp of the anomaly, assigning a category, and adding a short explanation, a process known as audio masking.

Thanks to open communication and clear labeling guidelines, our labelers delivered thousands of annotated clips within tight timelines. This workflow not only resolved integration hurdles quickly but also led to a 10/10 accuracy rating, consistent formatting, and seamless handoff to Reality Defender’s in-house systems.

Results: Meeting and Exceeding Expectations

Qualitative Improvements: Our structured and consistent data allowed Reality Defender to integrate results effortlessly into their existing systems. The collaborative effort streamlined workflows and resolved previous roadblocks.

Enhancing AI Detection with Efficient Data Labeling: In-House vs. Open Network Solutions

By optimizing data labeling workflows and implementing structured quality control, Sapien helped Reality Defender enhance the accuracy and efficiency of their AI detection models. Through a combination of refined processes and iterative improvements, the project achieved significant gains in performance and speed.

Sapien supported Reality Defender using two types of labeling teams: an in-house team for highly specialized data and a large external workforce for rapid, scalable labeling. This approach highlights the flexibility of Sapien's offerings. Companies can choose between or combine both models, leveraging in-house expertise for sensitive or complex data, and tapping into our global network of AI trainers for high-volume tasks with tight timelines.

99% Accuracy in Private AI Detection Model

Reality Defender’s private voice detection model, focused on distinguishing between human and non-human speakers, was powered by an internal team of four specialists. This in-house approach prioritized accuracy and consistency, resulting in an impressive 99% accuracy across 1,761 labeled audio clips. Completed in five days, this effort shows how a focused team can produce high-quality results when working with complex voice data.

3-Day Turnaround for 12,518 Public AI Detection Labels

For larger-scale labeling, Sapien activated its open network of external AI trainers. With 587 contributors, 12,518 data points were labeled in just three days. The internal accuracy for this phase was 68%, reflecting the natural trade-off between speed and fine-tuning while still achieving fast, reliable structuring at scale.

1-Day Completion for 5,450 Data Points

In another instance, 5,450 data points were completed in a single day by a group of 162 labelers from Sapien's open network. This round achieved an internal accuracy of 96.2%, demonstrating that even rapid labeling efforts can maintain a high standard when supported by structured workflows and clear guidelines.

Precise Data Labeling for Your AI Success

This collaboration with Reality Defender showcases the importance of tailored solutions and open communication in addressing complex data challenges. By delivering accurate and consistent labeled data, we supported their efforts to advance voice technology. 

If you’re ready to enhance your AI projects with dependable data labeling services, contact us today and let Sapien create solutions that meet your needs.