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This paper proposes a technique to improve the performance of spoken dialogue systems that not only consider knowledge about the semantic frames used by systems to understand the spoken language but also employ knowledge about the words in the system application domain that are used to complete frame slots. Using both knowledge sources, the technique considers specific word sequences to form what we call word-islands and which are then employed to create the language models and dictionary used by the system’s speech recogniser. Word-islands are easier to recognise than the words comprising the islands, which leads to improved spoken language understanding and system performance. Experiments have been conducted using two spoken dialogue systems which had previously been developed in our lab: one to provide fast food information and the other to provide bus travel information. Results show that the proposed technique improves the performance of both systems by improving speech recognition and spoken language understanding of sentence types that are difficult to process. [ABSTRACT FROM AUTHOR]

This research critiques Wertsch's truck-puzzle experiments by first questioning if those results constituted linguistic artifacts, and questioning if the resulting referential continuum comprised a legitimate developmental form. The participants in the original truck-puzzle experiments interacted using American English and Portuguese. To determine if the results were linguistic artifacts, they were compared with those of functionally related Japanese data, which were found to verify the original findings. Additionally, from this comparison, a new referential form was discovered. Finally, the original referential continuum was found to consist of forms that constitute change rather than development. Wertsch's work is valuable in that it refines and supplements Vygotsky's notion of external speech. This current research partially verifies Wertsch's findings, uncovers an additional referential form, argues for new developmental continua, and appends the results to Vygofsky's original developmental continuum, generating a more sensitive tool with which to analyze microgenesis. [ABSTRACT FROM AUTHOR]

This paper reports studies on annotating and automatically detecting dialog acts in human–human spoken dialogs. The work reposes on three hypotheses: first, the succession of dialog acts is strongly constrained; second, the initial word and semantic class of word are more important for identifying dialog acts than the complete exact word sequence of an utterance; third, most of the important information is encoded in specific entities. A memory based learning approach is used to detect dialog acts. For each utterance unit, eight dialog acts are systematically annotated. Experiments have been conducted using different levels of information, with and without the use of dialog history information. In order to assess the generality of the method, the specific entity tag based model trained on a French corpus was tested on an English corpus for a similar task and on a French corpus from a different domain. A correct dialog act detection rate of about 86% is obtained for the same domain/language condition and 77% for the cross-language or cross-domain conditions. [Copyright &y& Elsevier]

The aim of this contribution is to take a number of key notions in dialogical theory, and look for their possible counterparts in recent neuroscience. This comprises points like other-orientation, relationism and context-interdependence, embodiment of language, responsive understanding, potentialities, unfinalisability, implicitness and degrees of awareness, and redundancy of processing. The conclusion is that there is indeed an incipient convergence between neuro-biology and cognitive dynamics, on the one hand, and dialogism on the other. A general dialogical framework has something to offer to cognitive science and evolutionary psychology, not in the least because dialogue and dialogical interaction appear to be more fundamental than language (at least as language has been conceived most often). Brains are complex organisms coordinating the apperception of and interventions into the world, rather than producing and processing agency-free representations of the world. [Copyright &y& Elsevier]

Spoken dialogue systems (SDSs) can be used to operate devices, e.g. in the automotive environment. People using these systems usually have different levels of experience. However, most systems do not take this into account. In this paper, we present a method to build a dialogue system in an automotive environment that automatically adapts to the user’s experience with the system. We implemented the adaptation in a prototype and carried out exhaustive tests. Our usability tests show that adaptation increases both user performance and user satisfaction. We describe the tests that were performed, and the methods used to assess the test results. One of these methods is a modification of PARADISE, a framework for evaluating the performance of SDSs [Walker MA, Litman DJ, Kamm CA, Abella A (Comput Speech Lang 12(3):317–347, 1998)]. We discuss its drawbacks for the evaluation of SDSs like ours, the modifications we have carried out, and the test results. [ABSTRACT FROM AUTHOR]

DialogDesigner is an integrated design and development environment that supports dialogue designers in creating an electronic dialogue model, writing dialogue snippets, running and analysing simulation sessions, getting graphical views of the model, making automatic evaluation regarding dialogue model well-formedness, compiling the model into run-time code, and extracting different presentations. DialogDesigner has been used for research purposes as well as in commercial projects. Its primary focus is on providing support for the development process. We explain underlying ideas, illustrate the functionality of DialogDesigner and discuss its strengths. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of generating in-domain language model training data when little or no real user data are available. The two-stage approach taken begins with a data induction phase whereby linguistic constructs from out-of-domain sentences are harvested and integrated with artificially constructed in-domain phrases. After some syntactic and semantic filtering, a large corpus of synthetically assembled user utterances is induced. In the second stage, two sampling methods are explored to filter the synthetic corpus to achieve a desired probability distribution of the semantic content, both on the sentence level and on the class level. The first method utilizes user simulation technology, which obtains the probability model via an interplay between a probabilistic user model and the dialogue system. The second method synthesizes novel dialogue interactions from the raw data by modelling after a small set of dialogues produced by the developers during the course of system refinement. Evaluation is conducted on recognition performance in a restaurant information domain. We show that a partial match to usage-appropriate semantic content distribution can be achieved via user simulations. Furthermore, word error rate can be reduced when limited amounts of in-domain training data are augmented with synthetic data derived by our methods. [ABSTRACT FROM AUTHOR]

Focuses on the concept of the explicit performative utterances. Negation on the nature of the utterances as statements; Outline of grammatical theory; Consequences of treating explicit performative utterances as statements.