which contained the test vocabulary and many other words. The test set consisted of 262 utterances containing 44 cities and 2 states. The best result obtained on the test set was 92.9% word accuracy (90.1% on just the city names). Removing phoneme duration constraints reduced recognition accuracy to 82

We wish to: • Model and understand fashionability and its components • Provide recommendations and advice to users Novel dataset and Conditional Random Field model that can reason about different aspects of fashionability.

In this paper we address the problem of mismatch in train and test conditions. Counter intuitive as it may seem, we do this by employing a particular element from the well-known training paradigm of Minimum Classification Error training. Rather than recognising a sentence according to maximum likelihood, we examine a number of likelihood ratio-based word score techniques in order to rescore and resort N-best lists.

will be. When the recognition task consists of city names, this implies that the training corpus must consist of city names only. This causes problems, especially in the Netherlands, because there are not enough speech data available of the smaller cities or villages to satisfy the need for rare phonemes

This paper reports on a speech database that includes non-native pronunciation variants of city names/town names from several European languages. The database is designed as a research tool for the study of pronunciation variants in this specific domain that occur in different groups of non

likelihood, we examine a number of likelihood ratio-based word score techniques in order to rescore and resort N-best lists. Experiments for a Dutch city name recognition task did not lead to improved recognition performance. Analysing the results however, we see a number of promising handles for more

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This paper describes our recent work on mechanisms for error recovery in spoken dialogue systems. We focus on the acquisition of city names and dates in the flight reservation domain. We are specifically interested in addressing the issue of acquiring out-of-vocabulary city names through a speak

Abstract The KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KNOW-ITALL's novel architecture

The CrossTowns lexicons are part of a study that focuses on the phonetic variants that occur when speakers of different native languages (L1) with varying degrees of target language (L2) proficiency pronounce foreign city names. Based on a collection of speech data from this domain, it is one