Major progress is being recorded regularly on both the technology and exploitation of
automatic speech recognition (ASR) and spoken language systems. However, there are still …

In this paper, a framework for maximum a posteriori (MAP) estimation of hidden Markov
models (HMM) is presented. Three key issues of MAP estimation, namely, the choice of prior …

A complete overview of distant automatic speech recognition The performance of
conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon …

From the available biometric technologies, automatic speaker recognition is one of the most
convenient and accessible ones due to abundance of mobile devices equipped with a …

A trend in automatic speech recognition systems is the use of continuous mixture-density
hidden Markov models (HMMs). Despite the good recognition performance that these …

Presents a maximum-likelihood (ML) stochastic matching approach to decrease the acoustic
mismatch between a test utterance and a given set of speech models so as to reduce the …

Maximum a posteriori optimization of parameters and the Laplace approximation for the
marginal likelihood are both basis-dependent methods. This note compares two choices of …

Human-computer interface systems whose input is based on eye movements can serve as a
means of communication for patients with locked-in syndrome. Eye-writing is one such …

Recent advances in automatic speech recognition are accomplished by designing a plug-in
maximum a posteriori decision rule such that the forms of the acoustic and language model …

This paper introduces deep neural network (DNN)–hidden Markov model (HMM)-based
methods to tackle speech recognition in heterogeneous groups of speakers including …