# Speech to text evaluation
## Overview Speaker embeddings are fixed-dimensional vector representations that capture the unique characteristics of a speaker's voice, allowing for tasks such as speaker identification, verification, and diarization. Speaker embedding extraction is a crucial task in speaker recognition systems. It involves transforming variable-length audio signals into fixed-size vector representations that encapsulate speaker-specific information while being robust to variations in speech content, background noise, and recording conditions. ## Model Architecture: The default model used in this module (speechbrain/spkrec-ecapa-voxceleb) is based on the ECAPA-TDNN architecture, which has shown strong performance across various speaker recognition tasks. Other supported models include ResNet TDNN (speechbrain/spkrec-resnet-voxceleb) and xvector (speechbrain/spkrec-xvect-voxceleb). **Note**: Performance can vary significantly depending on the specific dataset, task, and evaluation protocol used. Always refer to the most recent literature for up-to-date benchmarks. ## Learn more: - [SpeechBrain](https://speechbrain.github.io/) - [ECAPA-TDNN](https://arxiv.org/abs/2005.07143) - [ResNet TDNN](https://doi.org/10.1016/j.csl.2019.101026) - [xvector](https://doi.org/10.21437/Odyssey.2018-15)