In speaker verification task, we often use EER to measure the performance of a deep learning model. However, if you also need to compute Recall, we will tell you how to do in this tutorial.
We have known that random seed value can affect the performance of a deep learning model. In this tutorial, we will discuss what random seed we should use when building an AI model.
When we are training a deep learning model, we may have to set a random seed to make the final result stable. In this tutorial, we will discuss the effects of random seed.
We are building a chinese TTS or ASR system, we should use text normalization. In this tutorial, we will introduce how to do.
When we are building a speaker verification model, we have to build a test set to evaluate the performance of our model. For example, you will use this test to compute EER or minDCF.
The Channel-wise squeeze-excitation module (SE module) has achieved a great success in both computer vision and speech processing fields. In this tutorial, we will introduce it for beginners.
In voiceprint and face recognition, one of the important things is to determine similarity threshold. In this tutorial, we will introduce you how to get this threshold value.
In voiceprint and face recognition, EER is an important metrics. How to compute it? In this tutorial, we will introduce you how to do.
In order to evaluate a voiceprint recognition model, we need compute eer metric. In this tutorial, we will introduce some metrics TPR, FPR, FAR, FRR to help you understand how to compute ERR.
In machine learning, we may see these metrics: TPR, FPR, Precision and Recall metrics. In this tutorial, we will introduce their relation.