Solution: Speaker Intelligence
Tell which voice said what.
A podcast isn't one voice. It's two, three, sometimes a panel of six. Most monitoring tools collapse that into a single anonymous audio stream. Gossip separates each voice and recognizes it again next time it appears, so a recurring host's mentions of your brand stack into a pattern, not a pile.
Diarization in every clip
Two hosts and a guest are three signals, not one.
Most spoken-media tools treat a podcast episode as a single audio stream. A mention of your brand by a sceptical guest reads identically to a mention by an enthusiastic host. Gossip separates speakers within every clip, so each voice's contribution lands as its own line of analysis. The result: when you read your dashboard, you see who in the room said what, even if their name isn't on the booking page.
Diarization in every clip
Two hosts and a guest are three signals, not one.
Most spoken-media tools treat a podcast episode as a single audio stream. A mention of your brand by a sceptical guest reads identically to a mention by an enthusiastic host. Gossip separates speakers within every clip, so each voice's contribution lands as its own line of analysis. The result: when you read your dashboard, you see who in the room said what, even if their name isn't on the booking page.
Diarized transcript
Tesla
Tesla
Tesla
Persistent voice identity
When the same voice comes back, we know.
Diarization in a single clip is useful. Recognizing the same voice next week, next month, and next quarter is what turns spoken media into a pattern. On any given source (a podcast, a YouTube channel, a Twitch stream) Gossip remembers each distinct voice and stacks every appearance into one continuous timeline. A recurring host's mentions of your brand build into a real signal. A guest who appears once stays a one-off. The dashboard tells the difference automatically.
Persistent voice identity
When the same voice comes back, we know.
Diarization in a single clip is useful. Recognizing the same voice next week, next month, and next quarter is what turns spoken media into a pattern. On any given source (a podcast, a YouTube channel, a Twitch stream) Gossip remembers each distinct voice and stacks every appearance into one continuous timeline. A recurring host's mentions of your brand build into a real signal. A guest who appears once stays a one-off. The dashboard tells the difference automatically.
Recurring voices on this source
How each voice is trending Coming
Track the shift before it becomes a story.
An influential voice on a major podcast quietly cooling on your brand over six episodes is a story your dashboards should be telling, not something you stumble onto when total volume finally moves. Because Gossip recognizes the same voice across episodes, each voice's tone is its own line on the chart. Repeat advocates, repeat critics, and the voices whose position is actively changing each surface as their own pattern.
How each voice is trending Coming
Track the shift before it becomes a story.
An influential voice on a major podcast quietly cooling on your brand over six episodes is a story your dashboards should be telling, not something you stumble onto when total volume finally moves. Because Gossip recognizes the same voice across episodes, each voice's tone is its own line on the chart. Repeat advocates, repeat critics, and the voices whose position is actively changing each surface as their own pattern.
Voice tone trends
Diarized transcript
Tesla
Tesla
Tesla
Recurring voices on this source
Voice tone trends
The difference
Before and after.
A podcast episode reads as one audio stream, every mention attributed to "the show," not the speaker.
Each speaker on the clip surfaced separately, with their own mention and sentiment data.
A recurring host and a one-off guest are indistinguishable in the dashboard.
Recurring voices accumulate into a real pattern; one-off voices stay one-off.
You only see a story is breaking when total mention volume spikes.
Coming feature: tone shifts on individual recurring voices visible long before total volume moves.
Voice-level analysis (if any) requires manual tagging episode by episode.
Persistent voice identity within each source is automatic. The same voice next month is recognized as the same voice.
What customers say
Real results, in their own words.
Gossip saves us time and is informative, allowing us to pick up on developments in the political debate more quickly.
Marius Doksheim
Head of Insight, Civita
The insight is precise, sometimes brutally so. It is extremely valuable to see how we succeed (or fail) in our quest for relevant visibility.
Paal Leveraas
CEO, Tilt.works
We managed to catch a potentially damaging conversation about our products thanks to Gossip.
Kamilla Abrahamsen
Communication Manager, Tomra Systems ASA
Included capabilities
See the voices behind the mentions.
We'll show you how Gossip separates voices across podcasts, video, and streams, and how the same voice surfaces again next week, next month, next quarter.
Request a demoWhat you'll get from the demo
- A live diarization walkthrough on a multi-speaker clip you choose.
- A view of recurring voices on a podcast or channel of your choice, each with mention history against a brand you name.
- A side-by-side comparison: how text-first tools see the same conversation vs. what voice-level tracking actually surfaces.
Related capabilities
Audio & Video Tracking
Monitor podcasts, YouTube, TikTok, Twitch, Stortinget, and social video in real time.
Sentiment & Emotion Analysis
Read tone, intent, and emotion in spoken content. Beyond positive, neutral, negative.
Topic & Entity Tracking
Track brands, products, and topics by meaning, not by keyword.