This paper presents an algebraic wavenumber identification method to identify wavenumbers under stochastic conditions. Stochastic condition results from the introduction of small perturbation which is referred uncertainty measurements points’ coordinates caused by operation faults or problems with experimental errors. The proposed compared two popular alternatives, namely: inhomogeneous wave co...

We present a project aimed at understanding the acoustic and prosodic correlates of confidence and uncertainty in spoken language. We elicited speech produced under varying levels of certainty and performed perceptual and statistical analyses on the speech data to determine which prosodic features (e.g., pitch, energy, timing) are associated with a speaker’s level of certainty and where these p...

This work studies the use of observation uncertainty measures for improving the speech recognition performance of an exemplar-based source separation based front end. To generate the observation uncertainty estimates for the enhanced features, we propose the use of heuristic methods based on the sparse representation of the noisy signal in the exemplar-based source separation algorithm. The eff...

Observation uncertainty techniques offer a way to dynamically compensate automatic speech recognizers to account for the information missing in real world scenarios. These techniques have been demonstrated to effectively be able to compensate multiple environment distortions and improve the integration of ASR systems with speech enhancement pre-processing through uncertainty propagation. Unfort...

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

Recently, I.Cohen has presented causal and noncausal algorithms to modify the classic decision-directed approach for prior SNR. It is well-known that prior SNR is critical to trade off the musical noise level and the audible clearness level in spectral subtraction speech enhancement. However, all these algorithms conflict with statistical signal model more or less. To adjust smoothing parameter...

Uncertainty is ubiquitous in natural human communication. Human listeners assess the speaker’s degree of uncertainty at any time in communication and use this information to shape dialogue. In contrast, currently available computer systems dealing with spoken language are usually not built to perform this task. The ability to detect uncertainty would likely lead to more natural human-computer d...

We consider the time complexity of reaching agreement in a semi-synchronous model of distributed systems, in the presence of omission and Byzantine failures. In our semi-synchronous model, processes have inexact knowledge about the time to perform certain primitive actions: messages arrive within time d of when they are sent and the time between two consecutive steps of any process is in the kn...