Speech recognition and blackboard expert systems.

Abstract

Spoken language is used by people to communicate naturally with one another. A simplistic view of the communication process is as follows. Person A wishes to communicate an idea to person B. The idea, initiated in the mind/brain of person A is encoded into speech signals by means of the person A's speech production mechanism, the vocal apparata in the vocal tract. Various kinds of noise may interfere with the speech signals as they travel to person B. The resulting signal is captured by person B's speech receiving mechanism, the ear. It is then analysed and decoded into a meaningful message by the brain of person B. This thesis concerns itself with the investigation of and attempt to automate the receiving and decoding of English sentences using a machine - that is to perform the task of person B in the above scenario using a computer. The aim is not only to produce a sequence of phonetic sounds, but to look at the problems of building in the 'mind of the machine', a picture of the meanings, intentions, absurdities and realities of the spoken message. The various models, algorithms and techniques of speech recognition and speech understanding systems are examined. Speech signals are captured and digitised by hardware. The digital samples are analysed and the important distinguishing features of all speech sounds are identified. These are then used to classify speech sounds in subsequent spoken words. The way speech sounds are joined together to form syllables and words introduces difficult problems to the automatic recognition process. Speech sounds are blurred, overlapped or left out due to the effects of coarticulation. Finally, natural language processing issues, such as the importance of syntax (the structure) and semantics (the meaning) of sentences, are studied. A system to control and unite all the above processing is considered. The blackboard expert system model of the widely reported HEARSAY-II speech recognition system is reviewed as the system with the best potential for the above tasks.