Artificial intelligence (AI) is one of the core drivers of industrial development and a critical
factor in promoting the integration of emerging technologies, such as graphic processing …

Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

Network anomaly detection plays a crucial role as it provides an effective mechanism to
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …

We propose an anomaly detection method using the reconstruction probability from the
variational autoencoder. The reconstruction probability is a probabilistic measure that takes …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Abstract Convolutional Neural Networks (CNNs) are an alternative type of neural network
that can be used to reduce spectral variations and model spectral correlations which exist in …

Audio-visual speech recognition (AVSR) system is thought to be one of the most promising
solutions for reliable speech recognition, particularly when the audio is corrupted by noise …

Most current speech recognition systems use hidden Markov models (HMMs) to deal with
the temporal variability of speech and Gaussian mixture models (GMMs) to determine how …

Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic
models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech …

Deep neural networks have recently become the gold standard for acoustic modeling in
speech recognition systems. The key computational unit of a deep network is a linear …