Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings [93, 94, 95] and technical reports [90, 91, 92]. The length of this thesis including appendices, references,...

The uncertainty in parameter estimation due to the adverse environments deteriorates the classification performance for speech recognition. It becomes crucial to incorporate the parameter uncertainty into decision so that the classification robustness can be assured. In this paper, we propose a novel linear regression based Bayesian predictive classification (LRBPC) for robust speech recognitio...

In this paper we propose to combine audio-visual speech recognition with inventory-based speech synthesis for speech enhancement. Unlike traditional filtering-based speech enhancement, inventory-based speech synthesis avoids the usual trade-off between noise reduction and consequential speech distortion. For this purpose, the processed speech signal is composed from a given speech inventory whi...

The mismatch between training and testing environments makes the necessity of speech recognizers to be adaptive both in acoustic modeling and decision making. Accordingly, the speech hidden Markov models (HMMs) should be able to incrementally capture the evolving statistics of environments using online available data. Also, it is necessary for speech recognizers to exploit the robust decision s...

Uncertainty relations play a significant role in drawing line between classical physics and quantum physics. Since the introduction by Heisenberg, these have been considerably explored. However, effect of entanglement on uncertainty was not probed. Berta et al. [Nat. Phys. 6, 659 (2010)] removed this gap deriving conditional-entropic relation presence entanglement. In same spirit, using inferre...

The emerging field of uncertainty-of-observation techniques has recently been successful in improving performance of automatic speech recognition in non-stationary noisy environments by considering the preprocessed feature vectors not as deterministic but rather as random variables containing noisy observations of the underlying, hidden, clean speech features. A number of resulting modification...