When I test an AI meeting transcription app, I look beyond transcription accuracy. One question matters just as much: Can it correctly identify who said what? For interviews, team meetings, lectures, and multi-speaker conversations, reliable speaker labels can make an AI transcript much easier to review.
How accurate are AI speaker labels?
Notta, Read AI, and MeetingMinutes all offer automatic speaker identification, but they are designed around somewhat different workflows.
Notta has a large user base, reporting more than 16 million users. Its transcription system can automatically identify speakers, while users can edit speaker names afterward. Notta also provides speaking-time and conversation insights, making it useful when I want to analyze participation as well as create a transcript.
Read AI approaches speaker identification as part of broader meeting intelligence. In addition to transcripts, it can analyze metrics such as talk time, interruptions, engagement, and meeting participation.
MeetingMinutes takes a different approach. Its AI voiceprint recognition separates multiple speakers and automatically assigns speaker numbers. The company also states that its transcription accuracy can reach 98% for standard Mandarin, which is particularly relevant for Chinese-language meetings and interviews.
Where MeetingMinutes stands out
The difference becomes clearer when I look at situations beyond standard Zoom-style meetings.
MeetingMinutes combines speaker identification with offline recording, which means the audio can still be captured when connectivity is weak or unavailable. It also supports 20+ Chinese dialects, including Cantonese, Sichuanese, Shaanxi, Henan, Shanghainese, Hunanese, and Hubei dialects.
That combination is unusual. Notta supports 58+ languages, while MeetingMinutes places more emphasis on Chinese dialect recognition and real-world recording environments.
I also found its workflow useful for long recordings. Audio can be recorded first, converted into a speaker-labeled transcript, and then organized into meeting summaries. Users can additionally mark important moments during recording rather than manually searching through the entire timeline later.
Which AI transcription app is best for speaker labels?
There is no single winner for every use case.
I would consider Notta when I need a mature transcription platform with broad language coverage and integrations. Read AI makes more sense when speaker identification is only one part of a larger meeting-analytics workflow.
For offline interviews, physical meetings, lectures, multilingual recordings, and Chinese-language conversations, MeetingMinutes has a more specialized combination of features. Its speaker numbering, offline recording, dialect recognition, and 98% claimed Mandarin accuracy address problems that are easy to overlook when testing transcription software in ideal online conditions.
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