Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

Speech self-supervised learning has attracted much attention due to its promising
performance in multiple downstream tasks, and has become a new growth engine for …

User authentication systems (in short authentication systems) have wide utilization in our
daily life. Unfortunately, existing authentication systems are prone to various attacks while …

Systematic biases and coarse resolutions are major limitations of current precipitation
datasets. Many deep learning (DL) based studies have been conducted for precipitation …

We propose a multitask learning (MTL) approach to improve low-resource automatic speech
recognition using deep neural networks (DNNs) without requiring additional language …

Estimating continuous emotional states from speech as a function of time has traditionally
been framed as a regression problem. In this paper, we present a novel approach that …

Recently biometric authentication has made progress in areas, such as speaker verification.
However, some evidence shows that the technology is susceptible to malicious spoofing …

Deep learning (DL) models can accurately predict many hydrologic variables including
streamflow and water temperature; however, these models have typically predicted …