Ka Hōʻike a me ka Hoʻopā

He aha ka Speaker Diarization?

ʻO ka diarization speaker ka hana o ka hoʻokaʻawale ʻana i kahi kahawai leo i nā ʻāpana e like me ka ʻike o ka mea haʻi. I nā huaʻōlelo maʻalahi, e pane ana ia i ka nīnau "hea i kamaʻilio 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.

Pehea e hana ai ka hoʻomaopopo ʻana i ka mea kākau

Ka hoʻohanaʻana i nā hihia no ka hōʻike leo

Ka Hōʻike Hoʻolaha ma 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.