Timo Baumann

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When dialogue systems, through the use of incremental processing, are not bounded anymore by strict, nonoverlapping turn-taking, a whole range of additional interactional devices becomes available. We explore the use of one such device, trial intonation. We elaborate our approach to dialogue management in incremental systems, based on the(More)
We present a model of semantic processing of spoken language that (a) is robust against ill-formed input, such as can be expected from automatic speech recognisers, (b) respects both syntactic and pragmatic constraints in the computation of most likely interpretations, (c) uses a principled, expressive semantic representation formalism (RMRS) with a(More)
In incremental spoken dialogue systems, partial hypotheses about what was said are required even while the utterance is still ongoing. We define measures for evaluating the quality of incremental ASR components with respect to the relative correctness of the partial hypotheses compared to hypotheses that can optimize over the complete input, the timing of(More)
In this paper we do two things: a) we discuss in general terms the task of incremental reference resolution (IRR), in particular resolution of exophoric reference, and specify metrics for measuring the performance of dialogue system components tackling this task, and b) we present a simple Bayesian filtering model of IRR that performs reasonably well just(More)
We describe the 2012 release of our “Incremental Processing Toolkit” (INPROTK)1, which combines a powerful and extensible architecture for incremental processing with components for incremental speech recognition and, new to this release, incremental speech synthesis. These components work fairly domainindependently; we also provide example implementations(More)
Incremental natural language understanding is the task of assigning semantic representations to successively larger prefixes of utterances. We compare two types of statistical models for this task: a) local models, which predict a single class for an input; and b), sequential models, which align a sequence of classes to a sequence of input tokens. We show(More)
We define the task of incremental or 0lag utterance segmentation, that is, the task of segmenting an ongoing speech recognition stream into utterance units, and present first results. We use a combination of hidden event language model, features from an incremental parser, and acoustic / prosodic features to train classifiers on real-world conversational(More)
Participants in a conversation are normally receptive to their surroundings and their interlocutors, even while they are speaking and can, if necessary, adapt their ongoing utterance. Typical dialogue systems are not receptive and cannot adapt while uttering. We present combinable components for incremental natural language generation and incremental speech(More)
We describe work done at three sites on designing conversational agents capable of incremental processing. We focus on the ‘middleware’ layer in these systems, which takes care of passing around and maintaining incremental information between the modules of such agents. All implementations are based on the abstract model of incremental dialogue processing(More)