EditLint AI detects active speakers from per-track audio levels, flags cross-talk, removes silences and fillers and adds chapter markers from your transcript automatically.
When every guest has their own microphone, lavalier, dedicated XLR channel or Rodecaster track, the audio tracks are a perfect signal for speaker activity. EditLint AI analyses the loudness envelope of each track independently using 1-second analysis windows and maps out exactly when each speaker is active across the full recording duration.
Unlike voice-print speaker diarisation (which struggles with similar-sounding voices and degrades over long recordings), per-track loudness analysis is deterministic and consistent. A speaker is active when their track is consistently above the noise floor and above the other active tracks in the same window. Speaker turns are recognised with a minimum turn duration of 1.5 seconds, avoiding jitter from brief laugh-responses and backchannels.
Cross-talk : moments where two speakers are simultaneously above their active-speech threshold. This creates the most difficult decisions in podcast editing. You can't cleanly cut either side without losing context. You need to hear it, decide what to keep and often record a pickup or pickup the host's follow-on.
EditLint AI flags every cross-talk moment as a high-severity timeline marker. The panel shows duration, the two overlapping tracks and how far each track is above threshold. You can jump through all cross-talk markers in sequence, making decisions one by one, much faster than discovering them by ear during a linear playback review.
Record each participant on their own audio track, the standard setup for any quality podcast recording. EditLint AI works from the per-track signals; if you've used a combined mix, speaker detection falls back to transcript-based diarisation.
EditLint AI analyses the full recording in under 60 seconds, mapping the loudness envelope for each track across the entire session. Speaker turn boundaries are calculated and cross-talk windows identified from the overlap data.
Speaker turn markers are placed at every transition point. Cross-talk markers flag the overlapping regions. Silence and filler cuts are applied to the duplicate sequence. Chapter markers from the transcript are added at topic shifts. All in one pass.
Podcast editing isn't just silence removal. A professional episode needs clean pacing, readable chapters, searchable captions and short-form clips to drive discovery. EditLint AI's Podcast Auto-Edit mode runs every relevant feature in a single coordinated pass.
Start a session, point it at your multi-track recording and by the time you've made a coffee EditLint AI has produced: a cleaned rough cut with silences and fillers removed; speaker turn markers so you know who's talking at every point; cross-talk flags for the moments that need your attention; chapter markers from topic shifts in the transcript; and a word-timed SRT on the caption track.
Professional podcast editing used to mean hours of tedious first-pass work before the creative editing even began. EditLint AI collapses that into 20 minutes, every time.