The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

This book will help readers understand fundamental and advanced statistical models and
deep learning models for robust speaker recognition and domain adaptation. This useful …

Performance of speaker identification (SID) systems is known to degrade rapidly in the
presence of mismatch such as noise and channel degradations. This study introduces a …

We present the recent advances along with an error analysis of the IBM speaker recognition
system for conversational speech. Some of the key advancements that contribute to our …

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 …

In this paper we describe the recent advancements made in the IBM i-vector speaker
recognition system for conversational speech. In particular, we identify key techniques that …

Linear discriminant analysis—probabilistic linear discriminant analysis (LDA-PLDA) is a
standard and effective backend in the field of speaker verification. The object of LDA is to …

Abstract Language mismatch represents one of the more difficult challenges in achieving
effective speaker verification in naturalistic audio streams. The portion of bi-lingual speakers …

i-Vector feature representation with probabilistic linear discriminant analysis (PLDA) scoring
in speaker recognition system has recently achieved effective permanence even on channel …

Linear Discriminant Analysis (LDA) has been used as a standard post-processing procedure
in many state-of-the-art speaker recognition tasks. Through maximizing the inter-speaker …