Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

Speaker verification has been widely and successfully adopted in many mission-critical
areas for user identification. The training of speaker verification requires a large amount of …

This paper presents the problems and solutions addressed at the JSALT workshop when
using a single microphone for speaker detection in adverse scenarios. The main focus was …

Speaker Verification still suffers from the challenge of generalization to novel adverse
environments. We leverage on the recent advancements made by deep learning based …

Performance degradation caused by noise has been a long-standing challenge for speaker
verification. Previous methods usually involve applying a denoising transformation to …

Speaker verification (SV) has recently attracted considerable research interest due to the
growing popularity of virtual assistants. At the same time, there is an increasing requirement …

Self-supervised contrastive learning (SSCL) has achieved a great success in speaker
verification (SV). All recent works treat within-utterance speaker embeddings (SE) to be …

With the increase in the availability of speech from varied domains, it is imperative to use
such out-of-domain data to improve existing speech systems. Domain adaptation is a …

Data augmentation is conventionally used to inject robustness in Speaker Verification
systems. Several recently organized challenges focus on handling novel acoustic …

The deep learning methodologies in state-of-the-art speaker recognition systems are
predominantly limited to the extraction of recording level embeddings. This is usually …