The exploitation of deep neural networks (DNNs) as descriptors in feature learning
challenges enjoys apparent popularity over the past few years. The above tendency focuses …

The paper provides a comprehensive overview of speech enhancement techniques and
their applications. It discusses challenges in non-stationary noise, reverberation, and …

As the population ages with advances in technology, health monitoring through early
detection is increasing. There are several approaches to the analysis, monitoring and …

Articulatory representation learning is the fundamental research in modeling neural speech
production system. Our previous work has established a deep paradigm to decompose the …

Deep Reinforcement Learning (deep RL) has gained tremendous success in gaming but it
has rarely been explored for Speech Emotion Recognition (SER). In the RL literature, policy …

Generating high-fidelity conditional audio samples and learning representation from
unlabelled audio data are two challenging problems in machine learning research. Recent …

Most of the research on data-driven speech representation learning has focused on raw
audios in an end-to-end manner, paying little attention to their internal phonological or …

The usage of digital data is one of the main characteristics of the Agriculture 4.0 era.
Different devices and sensors may be used to capture a variety of types of data that enable …

We propose novel deep speaker representation learning that considers perceptual similarity
among speakers for multi-speaker generative modeling. Following its success in accurate …

Voice assistive technologies have given rise to far-reaching privacy and security concerns.
In this paper we investigate whether modular automatic speech recognition (ASR) can …