Any2Text's built-in automatic speaker identification feature uses advanced voiceprint analysis technology to effectively distinguish the voice characteristics of different speakers in a conference scenario. In the audio processing of multi-person conversations, the system assigns an independent number (e.g. Speaker 1, Speaker 2) to each speaker and clearly labels these identifiers in the text paragraph corresponding to the timestamp.
The realization of this feature relies on the extraction and analysis of speech features by deep neural networks. By recognizing multi-dimensional features such as timbre, intonation, and speech rate, the system can maintain a recognition accuracy of up to 90% or more even when the speaker alternates several times. Users only need to check the relevant options in the transcription settings without any training or configuration.
In practice, this feature significantly improves the transcription efficiency in scenarios such as meeting minutes and interview recordings. Compared with manual recording, automatic speaker recognition reduces more than 80% of organizing time. The resulting text can be immediately used to produce documents such as meeting minutes and interview recordings, greatly simplifying the workflow.
This answer comes from the articleAny2Text: Free AI tool for converting audio and video to textThe
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