Alexander Gruenstein

Targeted help for spoken dialogue systems: intelligent feedback improves naive users' performance

We present experimental evidence that providing naive users of a spoken dialogue system with immediate help messages related to their out-of-coverage utterances improves their success in using the system. A grammar-based recognizer and a Statistical Language Model (SLM) recognizer are run simultaneously. If the grammar-based recognizer suceeds, the less accurate SLM recognizer hypothesis is not used. When the grammar-based recognizer fails and the SLM recognizer produces a recognition hypothesis, this result is used by the Targeted Help agent to give the user feedback on what was recognized, a diagnosis of what was problematic about the utterance, and a related in-coverage example. The in-coverage example is intended to encourage alignment between user inputs and the language model of the system. We report on controlled experiments on a spoken dialogue system for command and control of a simulated robotic helicopter.

A multimodal learning interface for sketch, speak and point creation of a schedule chart

We present a video demonstration of an agent-based test bed application for ongoing research into multi-user, multimodal, computer-assisted meetings. The system tracks a two person scheduling meeting: one person standing at a touch sensitive whiteboard creating a Gantt chart, while another person looks on in view of a calibrated stereo camera. The stereo camera performs real-time, untethered, vision-based tracking of the onlookers head, torso and limb movements, which in turn are routed to a 3D-gesture recognition agent. Using speech, 3D deictic gesture and 2D object de-referencing the system is able to track the onlookers suggestion to move a specific milestone. The system also has a speech recognition agent capable of recognizing out-of-vocabulary (OOV) words as phonetic sequences. Thus when a user at the whiteboard speaks an OOV label name for a chart constituent while also writing it, the OOV speech is combined with letter sequences hypothesized by the handwriting recognizer to yield an orthography, pronunciation and semantics for the new label. These are then learned dynamically by the system and become immediately available for future recognition.

Multi-human dialogue understanding for assisting artifact-producing meetings

In this paper we present the dialogue-understanding components of an architecture for assisting multi-human conversations in artifact-producing meetings: meetings in which tangible products such as project planning charts are created. Novel aspects of our system include multimodal ambiguity resolution, modular ontology-driven artifact manipulation, and a meeting browser for use during and after meetings. We describe the software architecture and demonstrate the system using an example multimodal dialogue.