Enhancing Conversational AI with Speaker Diarization Annotations

Client's Challenge & Our Solution

A leading conversational AI company approached FutureBeeAI to improve their dialogue processing systems with precise speaker diarization. Their goal was to accurately identify and segment speakers in multi-party conversations for applications in customer support and voice-driven analytics.

FutureBeeAI provided a tailored solution, delivering over 250,000 annotated audio segments with speaker diarization. We leveraged our expertise in audio annotation to develop stringent guidelines and onboard skilled annotators, ensuring high-quality labeling of speakers even in complex, overlapping conversations. The project was executed using our proprietary annotation platform, which allowed seamless integration with the client’s workflow.

Outcome & Features:

Delivered 250,000 annotated audio segments with speaker diarization.

Handled diverse audio data, including multi-party conversations, cross-talk scenarios, and varying accents.

Completed the project within 10 weeks of timeline.