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. Language: English