Computer Models of Speech Using Fuzzy Algorithms: (Advanced Applications in Pattern Recognition)

It is with great pleasure that I present this third volume of the series "Advanced Applications in Pattern Recognition." It represents the summary of many man- (and woman-) years of effort in the field of speech recognition by tne author's former team at the University of Turin. It combines the best results in fuzzy-set theory and artificial intelligence to point the way to definitive solutions to the speech-recognition problem. It is my hope that it will become a classic work in this field. I take this opportunity to extend my thanks and appreciation to Sy Marchand, Plenum's Senior Editor responsible for overseeing this series, and to Susan Lee and Jo Winton, who had the monumental task of preparing the camera-ready master sheets for publication. Morton Nadler General Editor vii PREFACE Si parva licet componere magnis Virgil, Georgics, 4,176 (37-30 B.C.) The work reported in this book results from years of research oriented toward the goal of making an experimental model capable of understanding spoken sentences of a natural language. This is, of course, a modest attempt compared to the complexity of the functions performed by the human brain. A method is introduced for conce1v1ng modules performing perceptual tasks and for combining them in a speech understanding system.

Table of Contents:
1. Computer Models for Speech Understanding.- 1.1 Motivations for speech understanding researches.- 1.2 Tasks, difficulties and types of models.- 1.3 A passive model for automatic speech recognition.- 1.4 Active models for speech understanding.- 1.5 On the use of fuzzy set theory.- 1.6 The structure of the book.- 2. Generation and Recognition of Acoustic Patterns.- 2.1 Speech generation.- 2.2 Techniques for generating acoustic patterns.- 2.3 Background on syntactic pattern recognition.- 2.4 Acoustic Cue Extraction for Speech Patterns.- 2.5 Classification of speech patterns.- 2.6 Automatic recognition of continuous speech.- 2.7 References.- 3. On the Use of Syntactic Pattern Recognition and fuzzy Set Theory.- 3.1 Introduction and motivations.- 3.2 The syntactic (structural) approach to the interpretation of speech patterns.- 3.3 The syntax for the recognition of the phonetic feature “vocalic”.- 3.4 Background on fuzzy set theory.- 3.5 Fuzzy relations and languages.- 3.6 Use of fuzzy algorithms for feature hypothesization.- 3.7 References.- 4. Design Principles for Controlling the Use of Structural Rules for Segmentation.- 4.1 The meaning of the meaning.- 4.2 The control problem in the segmentation process.- 4.3 Computation with linguistic probabilities.- 4.4 Segmentation of continuous speech into pseudo-syllabic nuclei.- 4.5 A parallel processing model for generating phoneme hypotheses.- 4.6 A review of previous work on phoneme recognition.- 4.7 References.- 5. Rules for Characterizing Sonorant Sounds.- 5.1 A fragmant of the structural knowledge source for pseudo-syllables.- 5.2 Extraction of detailed spectral features for sonorant sounds.- 5.3 Generation of hypotheses about vowels.- 5.4 Use of formants for the recognition of liquids and nasals.- 5.5 Detailedrecognition of nasal sounds.- 5.6 Structure of the procedural knowledge.- 5.7 References.- 6. Rules for Characterizing the Nonsonorant Sounds.- 6.1 Introduction.- 6.2 Recognition of the phonetic features of nonsonorant sounds.- 6.3 Bottom-up generation of phonemic hypotheses of plosive sounds.- 6.4 Rules for the recognition of plosive sounds.- 6.5 Experimental results.- 6.6 References.- 7. The Lexical Knowledge Source.- 7.1 Word recognition in continuous speech.- 7.2 Dynamic programming for matching word patterns of quasi-continuous feature vectors.- 7.3 Matching speech states.- 7.4 Word detection by the hypothesize-and-test paradigm.- 7.5 The lexical component as a problem solver.- 7.6 The structure of the lexical knowledge.- 7.7 Strategies for lexical access.- 7.8 Selection of candidates and hypothesis evaluation.- 7.9 Strategies for the generation of lexical hypotheses.- 7.10 References.- 8. On the Structure and Use of Task-Dependent Knowledge.- 8.1 Introduction.- 8.2 Finite-state language models.- 8.3 Measuring evidences.- 8.4 Search strategies.- 8.5 On the use of production systems for problem solving.- 8.6 Scheduling of interpretation processes based on approximate reasoning.- 8.7 Outline of a semantically-guided use of task-dependent knowledge.- 8.8 Evaluating language complexity.- 8.9 Review of recent work on task-dependent knowledge.- 8.10 References.- 9. Automatic Learning of Fuzzy Relations.- 9.1 Introduction.- 9.2 Formal definition of the problem and an example of application.- 9.3 A simple preliminary learning case.- 10. Towards a Parallel System.- 10.1 A new model for lexical access.- 10.2 Description of acoustic cues.- 10.3 The knowledge of the descriptor of the global spectral features.- 10.4 Conclusions.