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

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 …

The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …

Recently, discriminative training (DT) methods have achieved tremendous progress in
automatic speech recognition (ASR). In this survey article, all mainstream DT methods in …

There have been many attempts to classify imbalanced data, since this classification is
critical in a wide variety of applications related to the detection of anomalies, failures, and …

The field of automatic speech recognition (ASR) is discussed from the viewpoint of pattern
recognition (PR). This tutorial examines the problem area, its methods, successes and …

The context-independent deep belief network (DBN) hidden Markov model (HMM) hybrid
architecture has recently achieved promising results for phone recognition. In this work, we …

This paper presents a voice conversion technique using Deep Belief Nets (DBNs) to build
high-order eigen spaces of the source/target speakers, where it is easier to convert the …

In this article, we studied the objective functions of MMI, MCE, and MPE/MWE for
discriminative learning in sequential pattern recognition. We presented an approach that …

This paper presents a voice conversion (VC) method that utilizes the recently proposed
probabilistic models called recurrent temporal restricted Boltzmann machines (RTRBMs) …