Technical Implementation of Speaker Recognition
The speaker differentiation feature in Simple Subtitling utilizes the most advanced voiceprint recognition technology available:
- model architecture: ECAPA-TDNN (Emphasized Channel Attention, Propagation andAggregation in Time Delay Neural Network) is one of the best speaker verification models currently available
- Training data: The pre-trained models provided by the project are trained on a large multi-speaker dataset
- Accurate optimization: Users can access developer-optimized gender classification models from the Hugging Face platform to improve results
Experiments show that under ideal recording conditions, the speaker differentiation accuracy of the system can reach more than 90%. It is especially valuable for multi-person scenarios such as meeting recordings and interview videos.
This answer comes from the articleSimple Subtitling: an open source tool for automatically generating video subtitles and speaker identificationThe
May not be reproduced without permission:AI productivity tools " Simple Subtitling's Speaker Identification Function Based on ECAPA-TDNN Machine Learning Architecture
