We present an assembly cell consisting of two cooperating robots and a variety of sensors. It offers a number of complex skills necessary for constructing aggregates from elements of a toy construction set. A high degree of flexibility was achieved because the skills were realised only through sensory feedback not by resorting to fixtures or specialised tools. The operation of the cell is completely controlled through natural language. Results from experiments in cognitive sciences and computer linguistics were incorporated to integrate natural language with vision as well as to control the construction dialogue between a human instructor and the robotic system. The experimental setup is described, and a sample dialogue demonstrates the capabilities of the cell. A brief discussion of issues for further research concludes the paper.

A complete dialogue system within the task domain of making an appointment is presented.It is based on a semantic network representation of linguistic knowledge and a word recognition system that communicates with the interpretation component bidirectionally.System robustness is achieved using a special metascore that evaluates the advance of the linguistic interpretation.

A spoken language understanding and dialogue system in the domain of appointment schedule is presented.The system is capable of understanding complex times, e.g. it correctly combines discontinuous constituents and resolves ambiguities.A distributed representation of surface structure models and an incremental semantic analysis is used to manage the complexity.An elaborate evaluation of the system based on measurements of accuracy was carried out.Our approach combines pattern recognition with linguistic aspects forming a system of measurement consisting of word accuracy, constituent accuracy, and concept accuracy.

The analysis and interpretation of time constituents is a rather complex enterprise, since diverse time constituents are distributable in variable positions within an utterance. The first step in order to manage this complexity and variability is the syntactic analysis at phrase structure level. Within a utterance each time constituent is analyzed independently and tested for its syntactic coherence. A semantic interpretation of the time constituent has to follow. The second step consists of the analysis and interpretation at sentence structure level. The time interpretations need to be tested for consistency and merged into a single representation. Here it is usually possible to resolve ambiguities. As a last step, the interpretations of time constituents have to be merged at dialogue level. Although the system asks the user for verification of the time computed, users do seldom just reply ’ or ’. Mostly they add new information about time, and sometimes users correct the system’s interpretation without explicit negation. Thus, the merging of time constituents at dialogue level becomes a rather complex issue

The analysis and interpretation of time constituents is important for most applications of speech understanding systems. Problems can be caused by the varying distribution of constituents. A basic set of time constituents were found in a corpus of domain specific (train schedule) utterances. A distributed representation of surface structure models and an incremental semantic analysis is used to manage the complexity. The knowledge base of the speech understanding system that provides the framework for the analysis and interpretation of time constituents uses the semantic network language ERNEST. Keywords: Speech Understanding, Time Constituents 1. INTRODUCTION In a speech understanding system there are several domains of analysis and interpretation. Firstly, the system has to recognize single words in a torrent of speech sounds. Secondly, the system combines words to constituents, i.e. it performs a syntactic analysis. It also has to reconstruct the meaning of the utterance in questio...