Workflow evaluation demonstrates a 95–98% reduction in early-stage analysis burden for military field audio, suggesting scalable qualitative evaluation through human-in-the-loop AI processing.
Operational field experiments generate large volumes of audio that capture user feedback, mission discussion, troubleshooting, and human-machine interaction, but manual transcription and qualitative review are difficult to scale. This paper describes a human-in-the-loop workflow for accelerating analysis of operational recordings using Whisper transcription, timestamped SubRip Subtitle (SRT) outputs, AI-assisted evidence extraction, and subject matter expert (SME) review. The workflow was applied to approximately 800 hours of raw audio from a two-week military field experiment, reduced to 217 hours of usable, non-duplicative audio. AI-assisted processing generated searchable transcripts and approximately 1,000 candidate evidence phrases organized around human-machine teaming constructs. SMEs spot-checked approximately 20% of entries against transcript context and source audio. The full process required approximately 40 analyst labor-hours, compared with an estimated 868–2,170 hours for manual transcription and first-pass review. Results suggest a 95–98% reduction in early-stage analysis burden while preserving SME judgment and source traceability.
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DeMarco et al. (2026) studied this question.
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