By Renato De Mori (auth.)
It is with nice excitement that I current this 3rd quantity of the sequence "Advanced functions in trend Recognition." It represents the precis of many guy- (and woman-) years of attempt within the box of speech popularity by means of tne author's former crew on the collage of Turin. It combines the easiest ends up in fuzzy-set idea and synthetic intelligence to indicate tips to definitive ideas to the speech-recognition challenge. it truly is my desire that it'll develop into a vintage paintings during this box. I take this chance to increase my thank you and appreciation to Sy Marchand, Plenum's Senior Editor accountable for overseeing this sequence, and to Susan Lee and Jo Winton, who had the enormous activity of getting ready the camera-ready grasp sheets for book. Morton Nadler normal Editor vii PREFACE Si parva licet componere magnis Virgil, Georgics, 4,176 (37-30 B.C.) The paintings pronounced during this publication effects from years of study orientated towards the target of creating an experimental version in a position to figuring out spoken sentences of a average language. this can be, after all, a modest try in comparison to the complexity of the services played via the human mind. a style is brought for conce1v1ng modules appearing perceptual initiatives and for combining them in a speech realizing system.
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Extra resources for Computer Models of Speech Using Fuzzy Algorithms
1 1 For an alphabet Vcr of symbols: the source entropy is defined as follows: 27 GENERATION AND RECOGNITION OF ACOUSTIC PATTERNS teeth velum vocal folds a) nasal cavity lips --=-,I vocal cavity n L -_ _ _ _~. Fig. I vocal folds Speech production scheme Speech production model b) 28 CHAPTER 2 n H(cr) =! 1) and is measured in bits. 1 bits/symbol. 8 bits/symbol. If the occurrence of a symbol S may depend upon a finite number m of preceding symbols, the source is called an m-th order Markov source and the entropy is the average of information carried by each symbol, given its past m symbols.
3 A Passive Model for Automatic Speech Recognition A passive model for automatic speech recognition follows the scheme of Fig. 2. The microphone roughly corresponds to the ear, the cochlea may be considered as a sort of preprocessor performing sensation, the auditory nerve acts. as a feature extractor performing perception, the left cerebral cortex may contain the classifier, whose knowledge 1S a memory of templates. Dashed lines show the flow of information when the system is learning its templates for each class of patterns to be recognized.
By analogy to computer systems, the kpowledge sources and the hypotheses they permit can be viewed as components and information of a hierarchical machine. Fig. 3 shows a possible model for such a machine. The interactions between levels of the hierarchical machine mostly concern exchange of information instead of commands for evaluating functions. The latter situation is typical of operating systems where a level uses lower levels as virtual machines. Furthermore, in SUS, processes at various levels may operate con- CHAPTER 1 14 currently, provided that some conditions regarding the data they are to process are verified.