The authors give a detailed account of a system environment for treating general problems of image and speech understanding. A framework for representing declarative and procedural knowledge based on a suitable definition of a semantic network is provided. The syntax and semantics of the network are clearly defined. The pragmatics of the network in its use for pattern understanding are defined by several rules which are problem-independent. This allows one to formulate problem-independent control algorithms. Complete software environments are available to handle the described structures. The general applicability of the network system is demonstrated by short descriptions of three applications from different task domains. >

/pdf/ernest-a-semantic-network-system-for-pattern-understanding-5auky8mswo.pdf

ERNEST: a semantic network system for pattern understanding

One of the most obvious and natural applications for speech technology is in providing a gateway to information services over the telephone network. Already a significant growth is occurring the provision of information from a centralised computing system using stored messages or synthetic speech derived from text files. Most of these systems however currently rely on the use of touch tone input for selection of the information. In the UK only 15% of homes and businesses have tone dialling, although the network is currently able to support around 50% tone dialling. The ability to recognise a small number of words or even the digits without the user requiring to train the system therefore has widespread application. As the scope of the information service expands so also does the need for more intelligent dialogues with much larger vocabularies for speech understanding.

Speech Understanding and Dialogue over the telephone: an overview of the ESPRIT SUNDIAL project.

The authors address the choice of suitable subword units for the hidden Markov model (HMM)-based front-end of a speaker-independent large vocabulary continuous speech dialog system (EVAR). In contrast to the well-known approach of using context-dependent phone-like units (for instance generalized triphones) the authors developed inventories of larger-sized subword units, so-called context-freezing units (CFU). CFU models can be considered as an approximation to the extremely desirable situation of having whole word HMMs under the limiting conditions of the training speech data at hand. Recognition experiments indicate an advantage of the context-freezing units over triphone/biphone/phone combinations in terms of the achieved word accuracy, at least in the case of German speech. Using triphones with contexts generalized by means of broad phonetic classes, the authors achieved results comparable to the CFU ones. >

Acoustic modelling of subword units in the Isadora speech recognizer

Speech understanding and dialouge over the telephone: an overview of the ESPRIT SUNDIAL

In this paper we describe the linguistic processor of a spoken dialogue system. The parser receives a word graph from the recognition module as its input. Its task is to find the best path through the graph. If no complete solution can be found, a robust mechanism for selecting multiple partial results is applied. We show how the information content rate of the results can be improved if the selection is based on an integrated quality score combining word recognition scores and context-dependent semantic predictions. Results of parsing word graphs with and without predictions are reported.

pdf/robust-parsing-of-spoken-dialogue-using-contextual-knowledge-1rkfmtdjw7.pdf

Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities

This article describes the work in the development of the speech understanding and dialog system EVAR. The relevant knowledge bases containing the raw linguistic knowledge and the preprocessors converting this to the specialized form needed by the processing algorithms are treated. Processing so far covers the level of the speech signal up to the level of pragmatic analysis. Some topics of ongoing and future work are mentioned briefly.

A knowledge based speech understanding system

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A knowledge based speech understanding system