We consider the problem of achieving coordinated actions in a real-time distributed system. In particular, we consider how closely (in terms of real time) processors can be guaranteed to perform a particular action, in a system where message transmission is guaranteed, but there is some uncertainty in message transmission time. We present an algorithm to achieve optimal precision in arbitrary n...

 Return, risk and correlation estimates are fraught with uncertainty (the inability to know whether our estimates are correct, even if our method of estimation is unbiased).  Diversification, while always desirable, is especially valuable in the presence of uncertainty. In fact, uncertainty can render diversification a more ―optimal‖ solution than traditional mean-variance optimization.  Thi...

In a stochastic decision environment, di¤erences in information can lead rational decision makers facing the same stochastic technology and the same markets to make di¤erent production choices. E¢ ciency and productivity measurement in such a setting can be seriously and systematically biased by the manner in which the stochastic technology is represented. For example, conventional production f...

Stimulus uncertainty produced by variations in a target stimulus to be detected or discriminated, impedes perceptual learning under some, but not all experimental conditions. To account for those discrepancies, it has been proposed that uncertainty is detrimental to learning when the interleaved stimuli or tasks are similar to each other but not when they are sufficiently distinct, or when it o...

Although deep neural network (DNN) based acoustic models have obtained remarkable results, the automatic speech recognition (ASR) performance still remains low in noise and reverberant conditions. To address this issue, a speech enhancement front-end is often used before recognition to reduce noise. However, the front-end cannot fully suppress noise and often introduces artifacts that are limit...

This report presents uncertainty decoding as a method for robust automatic speech recognition for the Noise Robust Automatic Speech Recognition project funded by Toshiba Research Europe Limited. The effects of noise on speech recognition are reviewed and a general framework for noise robust speech recognition introduced. Common and related noise robustness techniques are described in the contex...

As synthetic voices become more flexible, and conversational systems gain more potential to adapt to the environmental and social situation, the question needs to be examined, how different modifications to the synthetic speech interact with each other and how their specific combinations influence perception. This work investigates how the vocal effort of the synthetic speech together with adde...

While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact in pattern recognition tasks has received relatively little attention to date. In this work we explicitly take into account feature measurement uncertainty and we show how classification rules should be adjusted to compensate for its effects. Our approach is pa...

A large-vocabulary continuous speech recognition (LVCSR) system usually utilizes a language model in order to reduce the complexity of the algorithm. However, the constraint also produces side-effects including low accuracy of the out-ofgrammar sentences and the error propagation of misrecognized words. In order to compensate for the side-effects of the language model, this paper proposes a nov...