From the available biometric technologies, automatic speaker recognition is one of the most
convenient and accessible ones due to abundance of mobile devices equipped with a …

Probabilistic linear discriminant analysis (PLDA) is commonly used in biometric
authentication. We review three PLDA variants—standard, simplified and two-covariance …

In real-world environments, noisy utterances with variable noise levels are recorded and
then converted to i-vectors for cosine distance or PLDA scoring. This paper investigates the …

The availability of multiple utterances (and hence, i-vectors) for speaker enrollment brings
up several alternatives for their utilization with probabilistic linear discriminant analysis …

In this study, we explore the propagation of uncertainty in the state-of-the-art speaker
recognition system. Specifically, we incorporate the uncertainty associated with observation …

This paper presents a Speech Technology Center (STC) system submitted to the NIST i-
vector Challenge. The system includes different subsystems based on PLDA, LDA-SVM …

The process of manually labeling data is very expensive and sometimes infeasible due to
privacy and security issues. This paper investigates the use of two algorithms for clustering …

While i-vector/PLDA framework has achieved great success, its performance still degrades
dramatically under noisy conditions. To compensate for the variability of i-vectors caused by …

Multicondition training (MCT) is an established technique to handle noisy and reverberant
conditions. Previous works in the field of i-vector based speaker recognition have applied …

The article deals with the compensation of variability in Automatic Speaker Verification
systems in scenarios where the variability conditions due to utterance duration …