Computer Models of Speech Using Fuzzy Algorithms

1. Front Matter

   Pages i-xxvi

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2. Computer Models for Speech Understanding

   • Renato De Mori

   Pages 1-24

3. Generation and Recognition of Acoustic Patterns

   • Renato De Mori

   Pages 25-66

4. On the Use of Syntactic Pattern Recognition and Fuzzy Set Theory

   • Renato De Mori

   Pages 67-121

5. Design Principles for Controlling the Use of Structural Rules for Segmentation

   • Renato De Mori

   Pages 122-171

6. Rules for Characterizing Sonorant Sounds

   • Renato De Mori

   Pages 172-227

7. Rules for Characterizing the Nonsonorant Sounds

   • Renato De Mori

   Pages 228-270

8. The Lexical Knowledge Source

   • Renato De Mori

   Pages 271-328

9. On the Structure and Use of Task-Dependent Knowledge

   • Renato De Mori

   Pages 329-399

10. Automatic Learning of Fuzzy Relations

    • Renato De Mori

    Pages 400-415

11. Towards a Parallel System

    • Renato De Mori

    Pages 416-439

12. Back Matter

    Pages 441-482

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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.

• algorithms
• artificial intelligence
• pattern
• pattern recognition
• speech recognition

• Concordia University, Montreal, Canada

  Renato Mori

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