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Interview repurposing fails when the tool cannot tell who spoke. Here is how SparkVox lets you pick your voice before posts generate.
You crushed a podcast interview. Forty-five minutes of sharp takes, stories, and frameworks. Then you upload the episode to a repurposing tool and get LinkedIn drafts that quote the host, open with their questions, or blend both voices into something that does not sound like you at all.
The problem is not the AI. The problem is whose words the AI is allowed to use.
Most repurposing tools treat a transcript as one voice. On a host-guest episode, roughly half the transcript is questions, reactions, and banter that should never become your LinkedIn posts. If the tool cannot tell who said what, it will happily turn the host's setup into your hook.
Podcast guests and advisors on client calls need a filter: only my lines from this conversation.
When you create a project with Guest (podcast) or Advisor (Knowledge & Advisory) perspective on audio or a YouTube URL, SparkVox:
awaiting_speaker status and notifies you in the appHost-led shows that use the full episode should use Full Conversation perspective instead - the full cleaned transcript drives the posts.
If you upload a plain .txt or .srt file, SparkVox uses Full Conversation or Trainer perspective and does not run speaker selection. For guest appearances or advisory calls, use the URL or audio upload so diarization can run.
Posts grounded in your lines read like you on the mic - not like a summary of a conversation you were part of. Combined with your voice profile and Source and Lens settings, expert-guest repurposing finally produces content you would actually publish under your name.
See SparkVox for expert guests or read the speaker selection help article.
A single conversation contains more content than most people publish in a month. Here's the system for extracting every piece of it, starting with the you capture right after.
One 45-minute episode contains more content than most hosts realize. Here is the system for extracting all of it, starting with the 60 seconds right after you stop recording.
Upload a transcript or recording as a SparkVox project, extract moments into LinkedIn posts in your voice, and review everything in your sprout tree before you publish.
SparkVox captures your expertise and makes it visible on LinkedIn - in your voice, automatically. Upload a recording or transcript, review drafts in your sprout tree, and publish when ready.
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