Word Confidence Estimation (WCE) for machine translation (MT) or automatic speech recognition (ASR) consists in judging each word in the (MT or ASR) hypothesis as correct or incorrect by tagging it with an appropriate label. In the past, this task has been treated separately in ASR or MT contexts and we propose here a joint estimation of word confidence for a spoken language translation (SLT) t...

This paper describes our work with the data distributed for the WMT’12 Confidence Estimation shared task. Our contribution is twofold: i) we first present an analysis of the data which highlights the difficulty of the task and motivates our approach; ii) we show that using non-linear models, namely random forests, with a simple and limited feature set, succeeds in modeling the complex decisions...

Visual-Inertial Odometry (VIO) utilizes an Inertial Measurement Unit (IMU) to overcome the limitations of Visual Odometry (VO). However, the VIO for vehicles in large-scale outdoor environments still has some difficulties in estimating forward motion with distant features. To solve these difficulties, we propose a robust VIO method based on the analysis of feature confidence in forward motion e...

This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to...

This article introduces and evaluates several different word-level confidence measures for machine translation. These measures provide a method for labeling each word in an automatically generated translation as correct or incorrect. All approaches to confidence estimation presented here are based on word posterior probabilities. Different concepts of word posterior probabilities as well as dif...

We study the problem of supervised classification of stem cell colonies and confidence estimation of the attained classification labels. The problem is investigated in the application context of heterogeneity labels of stem cell colonies observed by using fluorescent microscopy imaging. Given the features of colonies using numerous image statistics, we report the classification results using ad...

This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b) and Horowitz (2001). asciker and bsciker allow several options ...

Selecting well-recognized transcripts is critical if information retrieval systems are to extract business intelligence from massive spoken document databases. To achieve this goal, we target spoken document confidence measures that represent the recognition rates of each document. We focus on the incoherent word occurrences over several utterances in ill-recognized transcripts of spoken docume...

In confidence based reliability measurement we determine that we are nt least C confident that the probability of n program failing is less than or equal to c1 bound 8. The basic results of this approach are reviewed and several additional results introduced, including r/w adaptive sampling rheorem which shows how confidence can be computed when faults are correcred as they appear in the restin...

We propose and test a practical means of finding poor pronunciations and missing variants for large lexicons. We do so by statistically assessing the confidence of each phone in each pronunciation and comparing it with the statistical distribution of the same confidence metric for corresponding phones over the entire training corpus. A phone is targeted for correction for each word in which its...