Over the past decades, a particular focus is given to research on machine learning
techniques for speech processing applications. However, in the past few years, research …

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 …

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 …

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 …

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 …

Gaussian mixture models are currently the dominant technique for modeling the emission
distribution of hidden Markov models for speech recognition. We show that better phone …

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

The Handbook of Natural Language Processing, Second Edition presents practical tools
and techniques for implementing natural language processing in computer systems. Along …

In this study, modulation spectral features (MSFs) are proposed for the automatic recognition
of human affective information from speech. The features are extracted from an auditory …

Over the past few decades, there has been tremendous development in machine learning
paradigms used in automatic speech recognition (ASR) for home automation to space …