In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

Data security, complete system control, and missed storage and computing opportunities in
personal portable devices are some of the major limitations of the centralized cloud …

Despite the significant improvements in speaker recognition enabled by deep neural
networks, unsatisfactory performance persists under noisy environments. In this paper, we …

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 …

Domain adaptation aims to reduce the mismatch between the source and target domains. A
domain adversarial network (DAN) has been recently proposed to incorporate adversarial …

This paper introduces a Weighted-Correlation Principal Component Analysis (WCR-PCA)
for efficient transformation of speech features in speaker recognition. A Recurrent Neural …

Even though speaker recognition has gained significant progress in recent years, its
performance is known to be deteriorated severely with the existence of strong background …

In this paper, we present a Mixture Linear Prediction based approach for robust Gammatone
Cepstral Coefficients extraction (MLPGCCs). The proposed method provides performance …

The mismatch between enrollment and test utterances due to different types of variabilities is
a great challenge in speaker verification. Based on the observation that the SNR-level …