What metadata is included with call center speech datasets?

At FutureBeeAI, we believe call center data is more than just conversations; it's a structured, insight-rich asset. Metadata provides the context AI models need to understand the who, what, when, and why of each interaction.

A high-quality call center speech dataset typically includes the following metadata:

Speaker Details

• Unique IDs to distinguish agent and customer
• Demographics like age, gender, and regional attributes for accent diversity and personalization

Call Information

• Call ID, type (inbound, outbound), duration, and timestamps to support flow and performance analysis

Audio-Specific Features

• Labels for background noise or clipping
• Speaker segmentation markers and pause intervals

Emotions & Sentiment

• Tone labels (positive, neutral, negative) and emotional tags like frustration or satisfaction

Language & Accent

• Primary language spoken and accent metadata

Why does this matter?

Because raw audio without context is just sound. With metadata, it becomes a structured dataset capable of powering real-time analytics, advanced ASR/NLU models, and smarter customer experience tools.

FutureBee AI delivers metadata-rich datasets tailored to your AI goals designed for insight, compliance, and performance.

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