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This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilises a likelihood-based`Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native con®dence scores and on rejection statistics(More)
This paper analyzes the behavior of callers responding to a speech recognition system when prompted either with an open or a directed dialog strategy. The results of two usability studies with different caller populations are presented. Differences between the results from the two studies are analyzed and are shown to arise from the differences in the(More)
This paper investigates how to improve the acoustic modelling of non-native speech. For this purpose we present an adaptation technique to combine hidden Markov models of the source and the target language of a foreign language student. Such model combination requires a mapping of the mean vectors from target to source language. Therefore, three different(More)
Advances in commercially-available ASR technology have enabled the deployment of " How-may-I-help-you? " interactions to automate call routing. While often preferred to menu-based or directed dialog strategies, there is little quantitative research into the relationships among prompt style, task completion, user preference/satisfaction, and domain. This(More)
Commercial spoken dialogue systems traditionally have been static in the sense that once deployed, these applications only get updated as part of formal releases. Also, the creation of classification grammars in natural language call routing applications requires expensive manual annotation of caller intents. The work presented here introduces a process to(More)