A Noisy Label Filter based on GMM Binary Classification for Speaker Verification

Abstract

Noisy labels are inevitable in real-world datasets. These noisy labels cause deep neural networks to gradient descent towards the wrong direction, leading to performance degradation. In this paper, We propose an efficient method for filtering out noisy labels during training. We calculate an embedding center for each speaker and compute the cosine similarity between each audio embedding and its corresponding speaker embedding center. Subsequently, we use a Gaussian Mixture Model (GMM) to classify the samples into two categories based on their cosine similarities and filter out the class with lower similarity as noisy labels. These steps are iterated until training is complete. We conducted experiments on real-world datasets as well as scenarios with artificially added noisy labels of different types. The experimental results demonstrate that our method achieved notable performance.