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Conversational speech · LLM Evaluation

Conversational Speech Data for LLM Evaluation

Natural two-party and multi-party conversation recorded with separate channels per speaker, covering the overlaps, interruptions and turn-taking that scripted data never produces. Applied to llm evaluation, the specification is driven by one thing: rubric scores with confidence intervals.

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Evaluator scoring AI voice output against a rubric — Conversational Speech Data for LLM Evaluation
Service
Conversational speech
Use case
LLM Evaluation
Primary metric
Rubric scores with confidence intervals
01

Required data profile

  • Native-speaker rater panels per language
  • Rubric-based scoring with calibration
  • Overlapping assignments for agreement
Conversational speech — LLM Evaluation · written into the SOW before recordingChannelsOne channel per speaker, time-alignedDuration15-60 minutes per conversationTopicsScenario cards, free topics, or your domain scriptsOverlapPreserved and tagged, never edited outMetadataRelationship between speakers, familiarity, topic, settingYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
ChannelsOne channel per speaker, time-aligned
Duration15-60 minutes per conversation
TopicsScenario cards, free topics, or your domain scripts
OverlapPreserved and tagged, never edited out
MetadataRelationship between speakers, familiarity, topic, setting
Two speakers recording natural conversational speech data — supporting conversational speech data for llm evaluation
Two speakers recording natural conversational speech data
03

Process

  • Scenario design so conversations stay on-domain without becoming scripted
  • Speaker pairing by familiarity level as specified
  • Multi-channel recording with per-speaker isolation
  • Diarisation-ready packaging
  • Verbatim transcription with speaker turns and overlap tags
04

Metrics this feeds

  • Rubric scores with confidence intervals
  • Inter-rater agreement
  • Failure-mode distribution
05

Failure modes to design out

  • Raters who are fluent but not native in the variety
  • Rubrics written in English and applied to non-English output without localisation

Conversations are checked for naturalness, not just audio quality. Sessions that drift into reading are rejected.

06

Deliverables

  • Per-speaker channels plus mixed reference
  • Turn-level transcripts
  • Overlap and backchannel tags
  • Conversation metadata

Frequently asked

Is conversational speech the right service for llm evaluation?

It covers native-speaker rater panels per language. Most llm evaluation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.

What languages are available?

All 14 languages in the network plus Indian English accent bands.

How is the evaluation set handled?

Collected first, from speakers disjoint from the training cohort, so improvement is measurable.

Scope conversational speech for llm evaluation

Send the metric you need to move.

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