The objective of this paper is' open-set'speaker recognition of unseen speakers, where ideal
embeddings should be able to condense information into a compact utterance-level …

While deep learning models have made significant advances in supervised classification
problems, the application of these models for out-of-set verification tasks like speaker …

Abstract Language recognition based on embedding aims to maximize inter-class variance
and minimize intra-class variance. Previous researches are limited to the training constraint …

The NIST Speaker Recognition Evaluation-Conversational Telephone Speech (CTS)
challenge 2019 was an open evaluation for the task of speaker verification in challenging …

In this paper we present an efficient method for training models for speaker recognition
using small or under-resourced datasets. This method requires less data than other SOTA …

In the age of artificial intelligence, it is important for machines to figure out who is speaking
automatically and in what language-a task humans are naturally capable of. Developing …

In this report, we present the details of the systems submitted to task 2 Short Duration
Speaker Verification (SdSV) 2020 challenge. In this report, the standard MFCC features are …

Automatic speaker verification (ASV) has shown immense potential across domains like
security, forensic analysis, and human-computer interaction. However, real-world …