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Voice recording · LLM Evaluation

Voice Recording for AI Training for LLM Evaluation

Studio voice recording built for model training rather than broadcast: controlled acoustics, consistent mic distance, and reproducible session parameters across every speaker. 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 — Voice Recording for AI Training for LLM Evaluation
Service
Voice recording
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
Voice recording — LLM Evaluation · written into the SOW before recordingSample rate48 kHzBit depth24-bitMicrophoneLarge-diaphragm condenser, fixed distance, pop filterRoomTreated booth, RT60 under 0.3sProcessingNone. No compression, EQ, or noise reduction on delivered a…Your values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Sample rate48 kHz
Bit depth24-bit
MicrophoneLarge-diaphragm condenser, fixed distance, pop filter
RoomTreated booth, RT60 under 0.3s
ProcessingNone. No compression, EQ, or noise reduction on delivered audio
Session length30-90 minutes per speaker, with breaks logged
Field recording session with a rural speaker in India — supporting voice recording for ai training for llm evaluation
Field recording session with a rural speaker in India
03

Process

  • Session template definition so every studio in the network records identically
  • Speaker briefing and pronunciation calibration
  • Take-level monitoring with immediate re-record on defect
  • Per-session technical report
  • Delivery with unprocessed masters
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

Delivered audio is never processed. Training pipelines should see the raw capture so augmentation stays under your control.

06

Deliverables

  • Unprocessed WAV masters
  • Session logs
  • Take-level metadata
  • Speaker consent records

Frequently asked

Is voice recording 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 voice recording for llm evaluation

Send the metric you need to move.

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