We propose a novel confidence measure based on the marginalization of jointly estimated error cause probabilities. Conventional confidence measures directly score the reliability of recognition results. In contrast, our method first calculates joint confidence and error cause probabilities and then sums them with respect to the error cause patterns to obtain the marginal confidence probability....

We address the problem of localized error detection in Automatic Speech Recognition (ASR) output to support the generation of targeted clarifications in spoken dialogue systems. Localized error detection finds specific mis-recognized words in a user utterance. Targeted clarifications, in contrast with generic ‘please repeat/rephrase’ clarifications, target a specific mis-recognized word in an u...

An existing rule-based assertion classifier is ported from English to Swedish: pyConTextSwe. Evaluation on Swedish clinical texts shows that the English lexical resources are useful, but that there are assertion cues not obtainable in existing resources. Iterative error correction of cue lexicons improves results for the ported classifier. Overall final results are 82% F-score on a development ...

This paper investigates improving supervised word segmentation accuracy with unlabeled data. Both large-scale in-domain data and small-scale document text are considered. We present a unified solution to include features derived from unlabeled data to a discriminative learning model. For the large-scale data, we derive string statistics from Gigaword to assist a character-based segmenter. In ad...

Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an approach that exploits non-local information to improve the NER recall. Several kinds of non-local features encoding entity token occurrence, entity boundary and entity class are explored under Conditional ...

Quantitative modelling and analysis is common in safety engineering, but it is often criticised. Objections include the difficulty in acquiring probabilities (e.g. for human error), the dubious assumptions often needed to manipulate them (e.g. independence of events), and the inherent uncertainty involved in making decisions based on probabilistic predictions. Clearly, poor predictions are of l...

The demand for computer-assisted language learning systems that can provide corrective feedback on language learners’ speaking has increased. However, it is not a trivial task to detect grammatical errors in oral conversations because of the unavoidable errors of automatic speech recognition systems. To provide corrective feedback, a novel method to detect grammatical errors in speaking perform...

Spelling error is broadly classified in two categories namely non word error and real word error. In this paper a localized real word error detection and correction method is proposed where the scores of bigrams generated by immediate left and right neighbour of the candidate word and the trigram of these three words are combined. A single character position error model is assumed so that if a ...

OBJECTIVE To determine whether trunk position sense is impaired in people with poststroke hemiparesis. BACKGROUND Good trunk stability is essential for balance and extremity use during daily functional activities and higher level tasks. Dynamic stability of the trunk requires adequate flexibility, muscle strength, neural control, and proprioception. While deficits of trunk muscle strength hav...

In the analysis of clustered data with covariates measured with error, a problem of common interest is to test for correlation within clusters and heterogeneity across clusters. We examined this problem in the framework of generalized linear mixed measurement error models. We propose using the simulation extrapolation (SIMEX) method to construct a score test for the null hypothesis that all var...