Why “Who Said What” Matters (EdgeTier) 🗣️

2026-05-11 · Source: AssemblyAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

Speaker identification, or diarization, is crucial for analyzing call center interactions, particularly for distinguishing customer and agent utterances. This distinction is vital for understanding conversation content from the customer's perspective and assessing agent quality, including tone of voice. Modern call systems often provide stereo audio files with unlabeled left and right channels, posing a challenge for speaker identification. An initial assumption that the first speaker is the agent proved unreliable due to the prevalence of outbound calls in contact centers, which reverse this pattern. To address this, some systems are now leveraging Large Language Models (LLMs) via API to analyze full call transcripts and accurately identify agents and customers based on their dialogue.

Key takeaway

For Machine Learning Engineers developing call center analytics, relying solely on initial speaker assumptions for diarization is insufficient. You should integrate LLM-based analysis of full call transcripts to accurately identify agents and customers, especially given the mix of inbound and outbound calls. This approach ensures robust data for both customer intent analysis and agent performance evaluation.

Key insights

Accurate speaker identification is critical for analyzing call center interactions and assessing agent performance.

Principles

• First speaker assumption is unreliable.
• LLMs can infer speaker roles from transcripts.

Method

Utilize LLMs via API to analyze full call transcripts and determine speaker roles (agent vs. customer) based on conversational content.

In practice

• Analyze customer content for conversation topics.
• Assess agent quality using agent utterances.

Topics

• Speaker Identification
• Call Transcript Analysis
• Customer Service Analytics
• Large Language Models
• Speaker Diarization

Best for: Machine Learning Engineer, AI Engineer, NLP Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AssemblyAI.