This paper presents a learning and scoring framework based on neural networks for
speaker verification. The framework employs an autoencoder as its primary structure while …

Statistical language modeling (LM), which aims to capture the regularities in human natural
language and quantify the acceptability of a given word sequence, has long been an …

Phonotactic constraints can be employed to distinguish languages by representing a speech
utterance as a multinomial distribution or phone events. In the present study, we propose a …

(57) ABSTRACT A system and method are presented for selectively biased linear
discriminant analysis in automatic speech recognition systems. Linear Discriminant Analysis …

Speech data typically contain information irrelevant to automatic speech recognition (ASR),
such as speaker variability and channel/environmental noise, lurking deep within acoustic …

Linear discriminant analysis (LDA) is designed to seek a linear transformation that projects a
data set into a lower-dimensional feature space while retaining geometrical class …

In the past several decades, classifier-independent front-end feature extraction, where the
derivation of acoustic features is lightly associated with the back-end model training or …

Various methods for feature extraction and dimensionality reduction have been proposed in
recent decades, including supervised and unsupervised methods and linear and nonlinear …

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near
future. This phenomenon is mostly due to the daily collection of telecommunication data from …

This paper explores a novel large margin approach to learning a linear transform for
dimensionality reduction in speech recognition. The method assumes a trained Gaussian …