Control Structures and Theories of Interaction in Speech Understanding Systems

CONTROL STRUCTURES AND THEORIES OF INTERACTION IN SPEECII UNDEP~.WI'ANDING SYSTEMS E.J. Briscoe and B.K. Boguraev University of Cambridge, Computer Laboratory Corn Exchange Street, Cambridge CB2 3QG, England ABSTRACT lr: this paper, we approach the problem of organisation and control ip. automatic speech understanding systems firaT.ly, by presentin~ a theory of the non-serial interactions "~eces';ary between two processors in the system; namely, the morphosyntaetic and the prosodic, and secondly, by showing how, when generalised, this theory allows one to specify a highly efficient architecture for a speech understanding system with a simple control structure and genuinely independent components. The theory of non-serial interactions we present predicts that speech is temporally organised in a very specific way; that is, tee system would not function effectively if the temporal distribution of various types of information in speech were different. The architecture we propose is developed from a study of the task of speech, unde:standing and, furthermore, is specific to this task. Consequently, the paper argues that general problem solving methods are unnecessary for speech understanding. ! INTRODUCTION ]t is generally accepted that (he control structures of speech understanding systems (SUSs) must allow for non-serial interactions between different knowledge sources or components within the system. By r, on- serial interaction (NS1) we refer to communication which extends beyond the normal, serial, flow of information entailed by the tasks undertaken by each component. For example, the output of the word recognition system will provide the input to morphosyntactic analysis, almost by definition; however, the operation of the morpho.~yntaetic anaiyser .,~hould be constrained on some occasions by prosodic cues: say, that he:" is accented and followed by a "pause". whil,':'.t dog is not, in (1) Max gave her dog b4-';cuits. Similarly, the output of the morphosyntactic analyser will provide the input to scrnantie analysis, but on occasion, the operation of the rnorphosyntacLic analyser will be more efficient if it has access to information about the discourse: say, that the horse has no unique referent ip, (2) "/he horse raced past the barn fell, because this information will facilitate the reduced relative interpretation (see Crain & Steedman, in press). Thus, NSIs will be required between components which occur both before and after the morphosyntactie analyser in the serial chain of processors which constitute the complete SUS. NSls can be captured in a strictly serial, hierarchical model, in which the flow of information is always "upwards", by computing every possibility compatible with the input at each level of processing. However, this will involve much unnecessary computation within each separate component which could be avoided by utilising information already ten,:;orally available in the signal or context of utterance, ]::ut net part of the input to that level. An alternative architecture is the heterarchical system; this avoids such inefficiency, in principle, by allowing each component, to communicate with all other components in the system. However, controlling the flow of information and specifying the interfaces between components in such systems has proved very difficult (Rcddy & Erman, 1975). The most sophisticated …

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