An AI recording app can turn a meeting, interview, lecture, or voice memo into a transcript. The harder part starts after transcription: extracting decisions, separating speakers, preserving context, and turning spoken information into something that can be scanned visually.
This is where text and image summaries begin to differ.
A text summary may contain decisions, action items, and key topics. An image summary can reorganize the same information into an infographic or another visual structure. Not every recording app supports both outputs, and the distinction matters when the source material contains dense discussions, numbers, or multiple speakers.
Notta states up to 98 percent accuracy, with speaker identification and transcription across 58 languages. Its AI Notes can also turn meeting information into visual formats such as infographics.
The percentages should not be treated as a common benchmark. Recording quality, microphone distance, overlapping speech, accents, background noise, and language all affect the result.
Text summaries are not the same as visual summaries
Otter separates a conversation into a transcript and an AI-generated summary. Its summary can include key points and action items, while speaker identification helps associate dialogue with participants.
Fireflies follows a similar structure. Its meeting workspace combines the transcript with an AI summary and action items, while live transcription and speaker labels are available during meetings.
Fathom focuses on recorded online meetings. Its documented workflow includes transcription, structured summaries, action items, and queries across meeting records.
Granola uses a different capture model. It combines device audio with notes entered during the conversation, then uses transcript context to expand those notes. This makes the human note layer part of the final record rather than treating transcription as the entire source.
Notta adds a visual layer. Its documentation describes AI-generated summaries, chapters, action items, and infographics. A visual summary can be useful when the source contains several topics that need to be scanned rather than read line by line.
Where recording quality still breaks the workflow
A clean transcript does not guarantee a correct summary.
A meeting with four speakers talking over each other can produce incorrect speaker labels even when most words are recognized. A lecture recorded from the back of a room can lose technical terms because microphone distance affects the input signal. Mixed accents can also change entity names, numbers, and specialist vocabulary.
The same problem carries into image summaries. If the transcript changes a number from 15 to 50, an infographic built from that transcript can preserve the wrong value in a more visually prominent form.
For this reason, a practical evaluation should inspect three layers separately:
Audio capture: microphone distance, background noise, overlapping speech.
Transcription: word accuracy, speaker separation, language coverage.
Summary generation: factual consistency, action-item attribution, and whether visual elements preserve the original numbers and relationships.
Scenario differences
For online meetings with several speakers, Otter, Fireflies, and Fathom provide transcript-centered workflows with speaker or meeting context.
For a workflow that keeps human notes alongside machine-generated context, Granola uses a different model from fully automated meeting summaries.
For recordings where a visual representation is part of the required output, Notta documents infographic generation in addition to text summaries.
For multilingual recordings, the documented language ranges differ substantially. Fireflies states support for more than 100 languages, while Notta states 58 transcription languages and more than 40 translation languages.
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