4 accepted papers
Using clustering-driven annotations to train a neural network can be a tricky task because of label noise. In this paper, we propose a dynamic and adaptive label noise cleansing method, called AdaptiveDrop which combines both label noise filtering and correction simultaneously in cascade to combine…
Clustering is an unsupervised learning technique, which leverages a large amount of unlabeled data to learn cluster-wise representations from speech. One of the most popular self-supervised techniques to train a speaker verification system is to predict the pseudo-labels using clustering algorithms…
Extraction of a speaker embedding vector plays an important role in deep learning-based speaker verification. In this contribution, to extract speaker discriminant utterance level embeddings, we propose a hybrid neural network that employs both cross- and self-module attention pooling mechanisms. Mo…
Over the recent years, various self-supervised contrastive embedding learning methods for deep speaker verification were proposed. The performance of the self-supervised contrastive learning framework highly depends on the data augmentation technique, but due to the sensitive nature of speaker infor…