The emergence of large-margin softmax cross-entropy losses in training deep speaker
embedding neural networks has triggered a gradual shift from parametric back-ends to a …

Speaker Recognition is a challenging topic in Speech Processing research area. The
various models proposed have succeeded in achieving a fairly high level of accuracy in this …

Uncertainty modeling in speaker representation aims to learn the variability present in
speech utterances. While the conventional cosine-scoring is computationally efficient and …

In forensic voice comparison, deep learning has become widely popular recently. It is mainly
used to learn speaker representations, called embeddings or embedding vectors. Speaker …

Speaker identification has recently attracted considerable attention in speaker recognition.
Environmental noise and short utterance pose two challenges for accurate speaker …

Recently, the speaker recognition techniques have been widely attractive for their extensive
use in many fields, such as speech communications, domestic services, security and access …

Speech utterances recorded under differing conditions exhibit varying degrees of
confidence in their embedding estimates, ie, uncertainty, even if they are extracted using the …

Speech emotion recognition (SER) systems have evolved into an important method for
recognizing a person in several applications, including e-commerce, everyday interactions …

In Speaker, identifying or recognizing human voices is a challenging task. Recently, the
automatic speaker recognition technique has been developed by using deep learning …

Text-independent speaker recognition is widely used in identity recognition that has a wide
spectrum of applications, such as criminal investigation, payment certification, and interest …