གསལ་བཤད་པ་ངོས་འཛིན & Diarization

སྐད་བརྡ་རིག་པའི་སྐད་བརྡ་རིག་པ་ཟེར་བ་དེ་ག་དེ་ཡིན་ནམ།

Speaker diarization is the process of partitioning an audio stream into segments according to the identity of the speaker. In simpler terms, it answers the question "who spoke when?" 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.

གསལ་བཤད་པ་ངོས་འཛིན་བྱེད་པའི་ཐབས་ལམ་

སྒྲ་སྐད་ངོས་འཛིན་བྱེད་པའི་ ལག་ལེན་གྱི་གནད་དོན་

སྒྲ་སྐད་ངོས་འཛིན་བྱེད་པའི་ 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.