���xAcknowledgements and copyright notices1 Introduction1.1 About this book1.2 Purpose of this book1.3 Some reasons to use this book1.4 What's in the book (and what's not)1.5 Computational set-up needed for this book1.6 Computational skills that are necessary in order to use the book1.7 Free software suggestions1.8 Book structureSounds and numbers2.1 Preparatory assignments2.2 Solutions2.3 Sampling2.4 Quantization2.5 The sampling theorem2.6 Generating a signal2.7 Numeric data types2.8 The program2.9 Structure of a loop2.10 Structure of an array2.11 Calculating the cosine values2.12 Structure of the program2.13 Writing the signal to a fileChapter summaryFurther ExercisesFurther readingDigital filters and resonators3.1 Operations on sequences of numbers3.2 A program for calculating RMS amplitude3.3 Filtering3.4 A program for calculating running means of 43.5 Smoothing over a longer time-window3.6 Avoiding the need for long window 3.7 IIR filters in C3.8 Structure of the Klatt formant synthesizerChapter summaryExercisesFurther reading Frequency analysis and linear predictive coding4.1 Spectral analysis4.2 Spectral analysis in C4.3 Cepstral analysis4.4 Computation of the cepstrum in C4.5 Pitch tracking using cepstral analysis4.6 Voicing detection4.7 f0estimation by the autocorrelation method4.8 Linear predictive coding4.9 C programs for LPC analysis and resynthesis4.10 Trying it out4.11 Applications of LPCChapter SummaryFurther exercisesFurther readingFinite-state machines5. 1 Some simple examples5.2 A more serious example5.3 Deterministic and non-deterministic automata5.4 Implementation in Prolog5.5 Prolog's processing strategy and the treatmentof variables5.6 Generating strings5.7 Three possibly useful applications o{ that idea5.8 Another approach to describing finite-state machines5.9 Self-loops5.10 Finite-state transducers(FSTs)5.11 Using finite-state transducers to relate speech to phonemes5.12 Finite-state phonology5.13 Finite-state syntactic processingChapter summaryFurther exercisesFurther readingIntroduction to speech recognition techniques6.1 Architectures for speech recognition6.2 The pattern-recognition approach6.3 Dynamic time warping6.4 Applications6.5 Sources of variability in speechChapter summaryFurther readingProbabilistic finite-state models7.1 Introduction7.2 Indeterminacy: n-gram models for part-of-speech tagging ~7.3 Some probability theory for language modelling7.4 Markov models7.5 Trigram models7.6 Incompleteness of the training corpus7.7 Part-of-speech model calculations7.8 Using HMMs for speech recognition7.9 Chomsky's objections to Markov models and some rejoindersChapter summaryFurther readingParsing8.1 Introduction8.9 A demo8.3 Intuitive parsing8.4 Recursive descent parsing8.5 The simplest parsing program8.6 Difference lists8.7 Generating a parse tree8.8 Syllabification8.9 Other parsing algorithms8.10 Chart parsing8.11 Depth-first vs. breadth-first search8.19 Deterministic parsing, Marcus parsing and minimal commitment parsing8.13 Parallel parsingChapter summaryFurther readingUsing probabilistie grammars9.1 Motivations9.2 Probabilistic context-free grammars9.3 Estimation of rule probabilities9.4 A practical example9.5 A limitation of probabilistic context-free grammars9.6 Tree adjoining grammars����