In this invited paper, my overview material on the same topic as presented in the plenary
overview session of APSIPA-2011 and the tutorial material presented in the same …

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

In this paper, we study the problem of image-text matching. Inferring the latent semantic
alignment between objects or other salient stuff (eg snow, sky, lawn) and the corresponding …

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that
allows attention-driven, multi-stage refinement for fine-grained text-to-image generation …

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 …

We propose a novel context-dependent (CD) model for large-vocabulary speech recognition
(LVSR) that leverages recent advances in using deep belief networks for phone recognition …

This review gives a general overview of techniques used in statistical parametric speech
synthesis. One instance of these techniques, called hidden Markov model (HMM)-based …

Abstract Energy-Based Models (EBMs) capture dependencies between variables by
associating a scalar energy to each configuration of the variables. Inference consists in …

This paper presents a brief survey on Automatic Speech Recognition and discusses the
major themes and advances made in the past 60 years of research, so as to provide a …

Spoken language recognition refers to the automatic process through which we determine
or verify the identity of the language spoken in a speech sample. We study a computational …