语音语言处理导论【2025|PDF下载-Epub版本|mobi电子书|kindle百度云盘下载】

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1 Introduction1

1.1 About this book2

1.2 Purpose of this book2

1.3 Some reasons to use this book3

1.4 What's in the book(and what's not)5

1.5 Computational set-up needed for this book8

1.6 Computational skills that are necessary in order to use the book9

1.7 Free software suggestions10

1.8 Book structure10

2 Sounds and numbers13

2.1 Preparatory assignments14

2.2 Solutions21

2.3 Sampling24

2.4 Quantization24

2.5 The sampling theorem27

2.6 Generating a signal29

2.7 Numeric data types31

2.8 The program34

2.9 Structure of a loop35

2.10 Structure of an array37

2.11 Calculating the cosine values38

2.12 Structure of the program39

2.13 Writing the signal to a file40

Chapter summary43

Further Exercises43

Further reading46

3 Digital filters and resonators47

3.1 Operations on sequences of numbers48

3.2 A program for calculating RMS amplitude48

3.3 Filtering50

3.4 A program for calculating running means of 452

3.5 Smoothing over a longer time-window54

3.6 Avoiding the need for long window54

3.7 IIR filters in C61

3.8 Structure of the Klatt formant synthesizer62

Chapter summary68

Exercises68

Further reading69

4 Frequency analysis and linear predictive coding71

4.1 Spectral analysis72

4.2 Spectral analysis in C72

4.3 Cepstral analysis79

4.4 Computation of the cepstrum in C80

4.5 Pitch tracking using cepstral analysis83

4.6 Voicing detection86

4.7 f0 estimation by the autocorrelation method90

4.8 Linear predictive coding95

4.9 C programs for LPC analysis and resynthesis100

4.10 Trying it out106

4.11 Applications of LPC106

Chapter Summary109

Further exercises109

Further reading110

5 Finite-state machines111

5.1 Some simple examples112

5.2 A more serious example113

5.3 Deterministic and non-deterministic automata116

5.4 Implementation in Prolog118

5.5 Prolog's processing strategy and the treatment of variables129

5.6 Generating strings132

5.7 Three possibly useful applications of that idea134

5.8 Another approach to describing finite-state machines135

5.9 Self-loops137

5.10 Finite-state transducers(FSTs)139

5.11 Using finite-state transducers to relate speech to phonemes144

5.12 Finite-state phonology149

5.13 Finite-state syntactic processing153

Chapter summary156

Further exercises156

Further reading156

6 Introduction to speech recognition techniques157

6.1 Architectures for speech recognition158

6.2 The pattern-recognition approach166

6.3 Dynamic time warping168

6.4 Applications177

6.5 Sources of variability in speech181

Chapter summary182

Further reading183

7 Probabilistic finite-state models185

7.1 Introduction186

7.2 Indeterminacy:n-gram models for part-of-speech tagging187

7.3 Some probability theory for language modelling190

7.4 Markov models192

7.5 Trigram models198

7.6 Incompleteness of the training corpus202

7.7 Part-of-speech model calculations209

7.8 Using HMMs for speech recognition210

7.9 Chomsky's objections to Markov models and some rejoinders213

Chapter summary219

Further reading219

8 Parsing221

8.1 Introduction222

8.2 A demo222

8.3 'Intuitive'parsing223

8.4 Recursive descent parsing225

8.5 The simplest parsing program232

8.6 Difference lists233

8.7 Generating a parse tree236

8.8 Syllabification238

8.9 Other parsing algorithms242

8.10 Chart parsing242

8.11 Depth-first vs.breadth-first search245

8.12 Deterministic parsing,Marcus parsing and minimal commitment parsing246

8.13 Parallel parsing249

Chapter summary249

Further reading250

9 Using probabilistic grammars251

9.1 Motivations252

9.2 Probabilistic context-free grammars256

9.3 Estimation of rule probabilities258

9.4 A practical example261

9.5 A limitation of probabilistic context-free grammars267

9.6 Tree adjoining grammars268

9.7 Data-oriented parsing271

Chapter Summary272

Conclusion and suggestions for further reading272

Appendix:The American Standard Code for Information Interchange(ASCII)275

Glossary277

References293

Index299