Learn More
Incremental spoken dialogue systems, which process user input as it unfolds, pose additional engineering challenges compared to more standard non-incremental systems: Their processing components must be able to accept partial, and possibly subsequently revised input, and must produce output that is at the same time as accurate as possible and delivered with(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)
In this paper we do two things: a) we discuss in general terms the task of incre-mental 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)
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)
When dialogue systems, through the use of incremental processing, are not bounded anymore by strict, non-overlapping 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)
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 combin-able components for incremental natural language generation and incremental(More)
We describe the 2012 release of our " Incremen-tal 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 domain-independently; we also provide example(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)
We present a component for incremental speech synthesis (iSS) and a set of applications that demonstrate its capabilities. This component can be used to increase the responsivity and naturalness of spoken interactive systems. While iSS can show its full strength in systems that generate output incrementally, we also discuss how even otherwise unchanged(More)
We define the task of incremental or 0-lag 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)