Ka kitea te kaikōrero me te whakarārangitanga

He aha te Whakahauhautanga Kaikōrero?

Ko te whakawāteatanga kaikōrero te tukanga o te whakawāteatanga o tētahi rerenga oro ki ngā wāhanga e ai ki te tuakiri o te kaikōrero. I roto i ngā kupu ngāwari ake, ka whakautua te pātai "Wā i kōrero ai?" This is essential for multi-speaker recordings like meetings, interviews, podcasts, conference calls, and legal proceedings where knowing who said what is just as important as what was said.

STT.ai uses advanced neural speaker diarization models that can detect and label speakers in real time. The system creates speaker embeddings -- numerical representations of each voice's unique characteristics -- and clusters them to distinguish between different people. This works even when speakers have similar voices or frequently interrupt each other.

He pēhea te mahi o te kite kaikōrero

Ka whakamahia ngā take mō te kite kaikōrero

Whakamāramatanga Kaikōrero i runga i te STT.ai

Speaker detection is available on all paid plans. When you transcribe audio or video with speaker detection enabled, the transcript will include speaker labels inline with the text. You can also export speaker-labeled transcripts in all supported formats including SRT, VTT, DOCX, JSON, and PDF.

The system can detect up to 20 distinct speakers in a single recording. For best results, ensure each speaker has at least a few seconds of solo speech. Overlapping speech is handled but may reduce accuracy in heavily cross-talked segments.