語音語言處理導論（簡體書）

《語音語言處理導論》主要內容簡介：中國國內中俄翻譯研究也不遑多讓，大約在上世紀50年代中期便已開始。可惜的是，這些中文相關早期機器翻譯研究，由于硬件與軟件的限制，沒能延續下來。中文計算語言學研究比較有系統的進展，還要等到1986年；海峽兩岸在同一年成立了兩個致力于中文計算語言學基礎架構建立的研究群。北京大學的計算語言學研究所在朱德熙先生倡導下成立，隨后一段時間由陸儉明、俞士汶主持。而臺灣“中研院”的中文詞知識庫小組，由謝清俊創立，陳克健主持，黃居仁1987年返臺后加入。

作者：（美國）克勒曼（John Coleman） 合著者：常寶寶

導讀
Acknowledgements and copyright notices
1 Introduction
1.1 About this book
1.2 Purpose of this book
1.3 Some reasons to use this book
1.4 Whats in the book (and whats not)
1.5 Computational set-up needed for this book
1.6 Computational skills that are necessary in order to use the book
1.7 Free software suggestions
1.8 Book structure

Sounds and numbers
2.1 Preparatory assignments
2.2 Solutions
2.3 Sampling
2.4 Quantization
2.5 The sampling theorem
2.6 Generating a signal
2.7 Numeric data types
2.8 The program
2.9 Structure of a loop
2.10 Structure of an array
2.11 Calculating the cosine values
2.12 Structure of the program
2.13 Writing the signal to a file
Chapter summary
Further Exercises
Further reading

Digital filters and resonators
3.1 Operations on sequences of numbers
3.2 A program for calculating RMS amplitude
3.3 Filtering
3.4 A program for calculating running means of 4
3.5 Smoothing over a longer time-window
3.6 Avoiding the need for long window
3.7 IIR filters in C
3.8 Structure of the Klatt formant synthesizer
Chapter summary
Exercises
Further reading

Frequency analysis and linear predictive coding
4.1 Spectral analysis
4.2 Spectral analysis in C
4.3 Cepstral analysis
4.4 Computation of the cepstrum in C
4.5 Pitch tracking using cepstral analysis
4.6 Voicing detection
4.7 f0estimation by the autocorrelation method
4.8 Linear predictive coding
4.9 C programs for LPC analysis and resynthesis
4.10 Trying it out
4.11 Applications of LPC
Chapter Summary
Further exercises
Further reading

Finite-state machines
5. 1 Some simple examples
5.2 A more serious example
5.3 Deterministic and non-deterministic automata
5.4 Implementation in Prolog
5.5 Prologs processing strategy and the treatment
of variables
5.6 Generating strings
5.7 Three possibly useful applications o{ that idea
5.8 Another approach to describing finite-state machines
5.9 Self-loops
5.10 Finite-state transducers(FSTs)
5.11 Using finite-state transducers to relate speech to phonemes
5.12 Finite-state phonology
5.13 Finite-state syntactic processing
Chapter summary
Further exercises
Further reading

Introduction to speech recognition techniques
6.1 Architectures for speech recognition
6.2 The pattern-recognition approach
6.3 Dynamic time warping
6.4 Applications
6.5 Sources of variability in speech
Chapter summary
Further reading

Probabilistic finite-state models
7.1 Introduction
7.2 Indeterminacy: n-gram models for part-of-speech tagging ~
7.3 Some probability theory for language modelling
7.4 Markov models
7.5 Trigram models
7.6 Incompleteness of the training corpus
7.7 Part-of-speech model calculations
7.8 Using HMMs for speech recognition
7.9 Chomskys objections to Markov models and some rejoinders
Chapter summary
Further reading

Parsing
8.1 Introduction
8.9 A demo
8.3 Intuitive parsing
8.4 Recursive descent parsing
8.5 The simplest parsing program
8.6 Difference lists
8.7 Generating a parse tree
8.8 Syllabification
8.9 Other parsing algorithms
8.10 Chart parsing
8.11 Depth-first vs. breadth-first search
8.19 Deterministic parsing, Marcus parsing and minimal commitment parsing
8.13 Parallel parsing
Chapter summary
Further reading

Using probabilistie grammars
9.1 Motivations
9.2 Probabilistic context-free grammars
9.3 Estimation of rule probabilities
9.4 A practical example
9.5 A limitation of probabilistic context-free grammars
9.6 Tree adjoining grammars
……