Many studies have shown that people display an apparent overcon…dence. In particular, it is common for a majority of people to describe themselves as better than average. The literature takes for granted that this better-than-average e¤ect is problematic. We argue, however, that, even accepting these studies completely on their own terms, there is nothing at all wrong with a strict majority of ...

The Maximum a posteriori hypothesis is treated as the decoded truth in speech recognition. However, since the word recognition accuracy is not 100%, it is desirable to have an independent con dence measure on how good the maximum a posteriori hypothesis is relative to the spoken truth for some applications. E orts are in progress [1, 2, 3] to develop such con dence measures with the intent of a...

We study the problem of measuring the uncertainty of computable general equilibrium (CGE) (or RBC)-type model simulations associated with parameter uncertainty. We describe two approaches for building conŽ dence sets on model endogenous variables. The Ž rst uses a standard Wald-type statistic. The second approach assumes that a conŽ dence set (sampling or Baycsian) is available for the free par...

This paper describes our approach to the estimation of con dence in the words generated by a speech recognition system. We describe the models and the features employed for con dence estimation. In addition we discuss the characteristics of an information-theoretic metric for assessing the performance of the con dence measure. We provide a simple application of con dence measures in which we ra...

Con dence measures have been found to be useful for a number tasks within the eld of Automatic Speech Recognition (ASR). For example, the use of con dence measures has been reported in the utterance veri cation, keyword spotting and Out-of-Vocabulary (OOV) word spotting literature. In this report, it is shown that so called 'hybrid Arti cial Neural Network/Hidden Markov Model' (HMM/ANN) systems...

This note presents three ways of constructing simultaneous condence intervals for linear estimates of linear functionals in inverse problems, including \Backus-Gilbert" estimates. Simultaneous con dence intervals are needed to compare estimates, for example, to nd spatial variations in a distributed parameter. The notion of simultaneous con dence intervals is introduced using coin tossing as an...

Automatic speech recognition (ASR) systems produce transcriptions for audio which sometimes contain errors. It is useful to know how much condence may be placed in this output being correct. Condence estimation is concerned with obtaining scores which quantify this level of condence. e development and application of a principled, žexible framework using conditional random eld (CRF) models f...