Why Accurate Speaker Labels Matter in Call Transcripts (Full Transcript)

Speaker attribution enables intent and quality analytics; heuristics fail with outbound calls, so LLMs can infer agent vs customer roles reliably.
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[00:00:00] Speaker 1: I think I heard from basically everybody that who said what is really important in the transcript at the end of the day. I guess, maybe, can you elaborate on that? Is it more important to know who said what or that the transcript's perfectly accurate at the end of the day?

[00:00:13] Speaker 2: For us, that's imperative, right? So obviously, we're looking at customer utterances and agent utterances, and we're analyzing those from if the customer has more content in, what is this conversation about? The agent has more in the agent quality assessment side, so are they using the right tone of voice? Often, what you get in more modern call systems, you get a call file that'll be stereo, two channels, and you'll have left and right. Often, they're not labeled, but we're dealing a lot with inbound calls. So originally, we had an assumption that the first person who speaks is the agent, because they go, hello, you're through to TV travel, spelled wrong. But that only worked for a while, because then we started to see that in every contact editor, there's a proportion of calls that end up being outbound. So oh, you emailed in about the, someone answers, oh, hello, it's Ryan. Yeah, you emailed us earlier on about the thing, and then we had it all arsed with. We started using some of the LLM stuff in the API, actually, to take the full call transcript and kind of say, this is a call transcript from a contact editor. Your job is to, from what they say, work out who is the agent and who is the customer. So that worked quite well.

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Arow Summary
The speakers discuss why correctly attributing each utterance to the agent versus the customer is critical in call transcripts. Accurate speaker labeling enables separate analyses such as customer intent/content understanding and agent quality/tone evaluation. They note that relying on a simple heuristic (the first speaker is the agent) fails when calls are outbound or vary in structure, and that stereo call audio often has unlabeled channels. To address this, they used an LLM-based approach to infer who is the agent and who is the customer from the transcript content, which worked well.
Arow Title
Why speaker attribution matters more than perfect transcripts
Arow Keywords
speaker diarization Remove
agent vs customer attribution Remove
call transcripts Remove
contact center analytics Remove
agent quality assessment Remove
customer intent Remove
stereo call recordings Remove
inbound vs outbound calls Remove
LLM inference Remove
channel labeling Remove
Arow Key Takeaways
  • Correctly identifying who said what (agent vs customer) is essential for meaningful contact center analytics.
  • Different analyses depend on role attribution: customer utterances drive intent/topic insights; agent utterances drive quality and tone assessment.
  • Channel-separated (stereo) call recordings often lack reliable labels, making attribution non-trivial.
  • Simple heuristics like 'first speaker is the agent' break down when outbound calls appear.
  • Using an LLM to infer roles from transcript content can improve speaker attribution accuracy.
Arow Sentiments
Neutral: The tone is pragmatic and problem-solving, focusing on operational challenges (mislabeling in outbound calls) and a practical solution (LLM-based role inference) without strong emotional language.
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