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We report on a long term project on the phonetic decoding of continuous speech, within the framework of an expert system. Spectrogram reading is the field of expertise under study, although it is clear that an automatic phonetic decoder should not be in the form of an expert system. We expect major improvements in the recognition rate when expert's rules will be incorporated into existing systems. We describe here both the method used to retrieve the human expertise and results by an experienced spectrogram reader on 50 phonetically-balanced sentences. We also sketch various ways in which the expert rules can be validated through an "inference engine" and actually used in a system. Finally, we discuss the impact this approach has on the design.

We present in this paper a new approach to acoustic-phonetic decoding of continuous speech that consists of integrating the two most promising present techniques in the field, i.e. stochastic modeling and knowledge-based expert systems. Our group has been developing several systems based on these two techniques during the past 15 years or so. Our present goal is to mix both in order to improve the overall quality of automatic phonetic decoding. This paper is concerned with the first developments of the project. We first present the main characteristics of the components of the system and its general architecture. We also present and discuss preliminary results concerning the segmentation of the speech wave into phonetic units and the gross labeling of these segments.