In September of 1976, four speech understanding systems were demonstrated, signifying the end of a five‐year program of research and development sponsored by the Advanced Research Projects Agency (ARPA). The best performance was displayed by the Harpy system developed at Carnegie–Mellon University. Harpy satisfied a set of design goals that were specified at the beginning of the program, including the gal of understanding over 90% of a set of naturally spoken sentences composed from a 1000‐word lexicon. After defining the nature of the speech understanding problem, the four systems are described and critically evaluated. Based on this review, a structure for a next‐generation speech understanding system is proposed and parts of it are considered as a possible model of the early stages of speech perception. The perceptual model addresses the issue of lexical access and includes a decoding network composed of expected spectral sequences for all word strings of English.

Review of the ARPA speech understanding project

Computer Models of Speech Using Fuzzy Algorithms

A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs. Error correction is done by strongly biasing parsing toward expected meanings unless clear evidence from the input shows the current sentence is not expected. A dialogue acquisition and tracking algorithm is presented along with a description of its implementation in a voice interactive system. A series of tests are described that show the power of the error correction methodology when stereotypic dialogue occurs.

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The correction of ill-formed input using history-based expectation with applications to speech understanding

Recent advances in automatic speech recognition

The Signal Processing and Pattern Recognition Group in Nancy is developing the MYRTILLE System for recognizing and understanding sentences in artificial and natural languages. A model for the oral control of a telephone exchange with an artificial language was proposed and described at ICASSP 76. In this paper, we first present a comparative study of two syntactic models that we have used concurrently in this experiment : a classical top-down model and a generalized bottom-up, island-driven model. Then, we discuss the generalization of this model to natural language understanding system (e.g. for the oral consultation of a data base). Such a generalization involves a very different structure, which is described in details.

http://ieeexplore.ieee.org/iel6/8365/26347/01170303.pdf

Research towards speech understanding models for artificial and natural languages

This paper describes a connected speech understanding system being implemented in Nancy, thanks to the work done in automatic speech recognition since 1968. This system is made up of four parts : an acoustic recognizer which gives a string of phoneme-like segments from a spoken sentence, a syntactic parser which controls the recognition process, a word recognizer working on words predicted by the parser and a dialog procedure which takes in account semantic constraints in order to avoid some of the errors and ambiguities. Some original features of the system are pointed out : modularily (e.g. the language used is considered as a parameter), possibility of processing slightly syntactically incorrect sentences, ... The application both in data management and in oral control of a telephone center has given very promising results. Work is in progress for generalizing our model : extension of the vocabulary and of the grammar, multi-speaker operation, etc.

J.-P. Haton

Papers

Organization and operation of a connected speech understanding system at lexical, syntactic and semantic levels