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Computer Models of Speech Using Fuzzy Algorithms

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 co...

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Bibliographic Details
Main Author: de Mori, Renato
Format: eBook
Language:English
Published: New York, NY Springer US 1983, 1983
Edition:1st ed. 1983
Series:Advanced Applications in Pattern Recognition
Subjects:
Computational Chemistry
Chemistry / Data Processing
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 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
  • 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

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