Computer Models of Speech Using Fuzzy Algorithms

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Product Details

  • ISBN-13: 9781461337447
  • Publisher: Springer US
  • Publication date: 11/28/2011
  • Series: Advanced Applications in Pattern Recognition Series
  • Edition description: Softcover reprint of the original 1st ed. 1983
  • Pages: 514
  • Product dimensions: 6.69 (w) x 9.61 (h) x 1.03 (d)

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.- 1.4.1. Elementary psychoacoustic considerations.- 1.4.2. Interpreting the acoustic signal is problem solving.- 1.4.3. Structures for Speech Understanding System models.- 1.4.4. Functional description of the system.- 1.4.5. On the tools for representing and using knowledge.- 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.- 2.2.1. The filter bank.- 2.2.2. The Fast Fourier Transform.- 2.2.3. Identification of the vocal tract parameters.- 2.2.4. Extraction of articulatory parameters.- 2.2.5. On the use of spectral representation of speech.- 2.3 Background on syntactic pattern recognition.- 2.4 Acoustic Cue Extraction for Speech Patterns.- 2.4.1 Silence interval between two sounds in a word.- 2.4.2 Quasi-stationary portions of the acoustic pattern.- 2.4.3 Lines.- 2.5 Classification of speech patterns.- 2.5.1 A brief history of automatic recognition of isolated words.- 2.5.2 The dynamic programming approach.- 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.- 3.4.1 Definition of fuzzy sets.- 3.4.2 Operations on fuzzy sets.- 3.4.3 Fuzzy restrictions.- 3.4.4 Possibility distributions.- 3.4.5 A simple example.- 3.5 Fuzzy relations and languages.- 3.5.1 Fuzzy relations.- 3.5.2 The extension of principle.- 3.5.3 Fuzzy languages.- 3.6 Use of fuzzy algorithms for feature hypothesization.- 3.6.1 Fuzzy algorithms.- 3.6.2 An example of application.- 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.- 4.4.2 Introduction.- 4.4.2 The segmentation grammar.- 4.4.3 The segmentation algorithm.- 4.4.4 Examples.- 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.- 5.1.1 Generalities.- 5.1.2 Generation of hypotheses about sonorant sounds.- 5.2 Extraction of detailed spectral features for sonorant sounds.- 5.2.1 Extraction of a multilinked data structure from a spectrogram.- 5.2.2 Deletion of unsuitable links.- 5.2.3 Assignment of weights to the arcs.- 5.3 Generation of hypotheses about vowels.- 5.3.1 Algorithm SZDET.- 5.3.2 Recognition of the place of articulation of vowels.- 5.3.3 Hypothesis generation and problem solving.- 5.4 Use of formants for the recognition of liquids and nasals.- 5.4.1 Liquid-nasal classification.- 5.4.2 Applications to the classification of liquids.- 5.5 Detailed recognition of nasal sounds.- 5.5.1 Introductory acoustical and perceptual considerations.- 5.5.2 Inference of the recognition rules.- 5.5.2.1 Speech material.- 5.5.2.2 Parameters of the atomic questions.- 5.5.2.3 The recognition rules.- 5.5.3 Experimental results.- 5.5.4 On the extension of the rules to other contexts.- 5.5.5 On the evaluation of binary features.- 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.- 6.3.1 Review of research concerning the plosive consonants.- 6.3.2 Recognition of plosive sounds.- 6.4 Rules for the recognition of plosive sounds.- 6.4.1 Rules for formant loci, formant slopes and burst spectra.- 6.4.2 Rules for spectral characteristics of plosives.- 6.4.3 Rules for formant features.- 6.4.4 Rules for phonemic hypotheses.- 6.4.5 Composition,of evidences.- 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.- 7.3.1 Minimum-distance models.- 7.3.2 Shastic models.- 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.- 7.7.1 Top-down constraints.- 7.7.2 Preconditions based on the first syllable.- 7.7.3 Precondition degradations.- 7.7.4 The lexicon as a content-addressable-memory.- 7.7.5 The syll-type tree.- 7.7.6 Precondition evidences.- 7.7.7 The algorithm for lexical access.- 7.8 Selection of candidates and hypothesis evaluation.- 7.8.1 Evaluation of precondition evidences.- 7.8.2 Candidate selection.- 7.8.3 Other possible methods for 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.- 8.4.1 Branch-and-bound algorithms.- 8.4.2 Non-admissible search algorithms.- 8.5 On the use of production systems for problem solving.- 8.6 Scheduling of interpretation processes based on approximate reasoning.- 8.6.1 Background.- 8.6.2 On the use of truth functions and fuzzy logic.- 8.6.3 Priority assignment and approximate reasoning.- 8.7 Outline of a semantically-guided use of task-dependent knowledge.- 8.7.1 System organization.- 8.7.2 The semantic knowledge.- 8.7.3 The syntactic knowledge.- 8.7.4 Pragmatics.- 8.8 Evaluating language complexity.- 8.9 Review of recent work on task-dependent knowledge.- 8.9.1 Representation.- 8.9.2 Control of strategies and scoring philosophies.- 8.10 References.- 9. Automatic Learning of Fuzzy Relations.- 9.1 Introduction.- 9.2 Formal definition of the problem and an example of application.- 9.2.1 Generalities.- 9.2.2 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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