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| Preface to the First Edition | |
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| Preface to the Second Edition | |
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| List of Abbreviations | |
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| Human Speech Communication | |
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| Value of speech for human-machine communication | |
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| Ideas and language | |
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| Relationship between written and spoken language | |
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| Phonetics and phonology | |
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| The acoustic signal | |
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| Phonemes, phones and allophones | |
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| Vowels, consonants and syllables | |
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| Phonemes and spelling | |
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| Prosodic features | |
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| Language, accent and dialect | |
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| Supplementing the acoustic signal | |
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| The complexity of speech processing | |
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| Chapter 1 summary | |
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| Chapter 1 exercises | |
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| Mechanisms and Models of Human Speech Production | |
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| Introduction | |
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| Sound sources | |
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| The resonant system | |
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| Interaction of laryngeal and vocal tract functions | |
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| Radiation | |
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| Waveforms and spectrograms | |
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| Speech production models | |
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| Excitation models | |
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| Vocal tract models | |
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| Chapter 2 summary | |
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| Chapter 2 exercises | |
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| Mechanisms and Models of the Human Auditory System | |
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| Introduction | |
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| Physiology of the outer and middle ears | |
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| Structure of the cochlea | |
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| Neural response | |
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| Psychophysical measurements | |
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| Analysis of simple and complex signals | |
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| Models of the auditory system | |
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| Mechanical filtering | |
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| Models of neural transduction | |
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| Higher-level neural processing | |
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| Chapter 3 summary | |
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| Chapter 3 exercises | |
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| Digital Coding of Speech | |
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| Introduction | |
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| Simple waveform coders | |
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| Pulse code modulation | |
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| Deltamodulation | |
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| Analysis/synthesis systems (vocoders) | |
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| Channel vocoders | |
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| Sinusoidal coders | |
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| LPC vocoders | |
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| Formant vocoders | |
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| Efficient parameter coding | |
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| Vocoders based on segmental/phonetic structure | |
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| Intermediate systems | |
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| Sub-band coding | |
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| Linear prediction with simple coding of the residual | |
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| Adaptive predictive coding | |
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| Multipulse LPC | |
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| Code-excited linear prediction | |
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| Evaluating speech coding algorithms | |
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| Subjective speech intelligibility measures | |
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| Subjective speech quality measures | |
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| Objective speech quality measures | |
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| Choosing a coder | |
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| Chapter 4 summary | |
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| Chapter 4 exercises | |
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| Message Synthesis from Stored Human Speech Components | |
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| Introduction | |
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| Concatenation of whole words | |
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| Simple waveform concatenation | |
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| Concatenation of vocoded words | |
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| Limitations of concatenating word-size units | |
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| Concatenation of sub-word units: general principles | |
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| Choice of sub-word unit | |
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| Recording and selecting data for the units | |
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| Varying durations of concatenative units | |
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| Synthesis by concatenating vocoded sub-word units | |
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| Synthesis by concatenating waveform segments | |
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| Pitch modification | |
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| Timing modification | |
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| Performance of waveform concatenation | |
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| Variants of concatenative waveform synthesis | |
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| Hardware requirements | |
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| Chapter 5 summary | |
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| Chapter 5 exercises | |
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| Phonetic synthesis by rule | |
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| Introduction | |
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| Acoustic-phonetic rules | |
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| Rules for formant synthesizers | |
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| Table-driven phonetic rules | |
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| Simple transition calculation | |
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| Overlapping transitions | |
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| Using the tables to generate utterances | |
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| Optimizing phonetic rules | |
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| Automatic adjustment of phonetic rules | |
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| Rules for different speaker types | |
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| Incorporating intensity rules | |
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| Current capabilities of phonetic synthesis by rule | |
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| Chapter 6 summary | |
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| Chapter 6 exercises | |
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| Speech Synthesis from Textual or Conceptual Input | |
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| Introduction | |
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| Emulating the human speaking process | |
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| Converting from text to speech | |
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| TTS system architecture | |
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| Overview of tasks required for TTS conversion | |
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| Text analysis | |
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| Text pre-processing | |
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| Morphological analysis | |
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| Phonetic transcription | |
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| Syntactic analysis and prosodic phrasing | |
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| Assignment of lexical stress and pattern of word accents | |
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| Prosody generation | |
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| Timing pattern | |
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| Fundamental frequency contour | |
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| Implementation issues | |
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| Current TTS synthesis capabilities | |
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| Speech synthesis from concept | |
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| Chapter 7 summary | |
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| Chapter 7 exercises | |
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| Introduction to automatic speech recognition: template matching | |
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| Introduction | |
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| General principles of pattern matching | |
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| Distance metrics | |
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| Filter-bank analysis | |
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| Level normalization | |
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| End-point detection for isolated words | |
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| Allowing for timescale variations | |
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| Dynamic programming for time alignment | |
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| Refinements to isolated-word DP matching | |
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| Score pruning | |
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| Allowing for end-point errors | |
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| Dynamic programming for connected words | |
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| Continuous speech recognition | |
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| Syntactic constraints | |
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| Training a whole-word recognizer | |
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| Chapter 8 summary | |
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| Chapter 8 exercises | |
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| Introduction to stochastic modelling | |
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| Feature variability in pattern matching | |
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| Introduction to hidden Markov models | |
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| Probability calculations in hidden Markov models | |
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| The Viterbi algorithm | |
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| Parameter estimation for hidden Markov models | |
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| Forward and backward probabilities | |
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| Parameter re-estimation with forward and backward probabilities | |
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| Viterbi training | |
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| Vector quantization | |
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| Multi-variate continuous distributions | |
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| Use of normal distributions with HMMs | |
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| Probability calculations | |
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| Estimating the parameters of a normal distribution | |
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| Baum-Welch re-estimation | |
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| Viterbi training | |
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| Model initialization | |
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| Gaussian mixtures | |
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| Calculating emission probabilities | |
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| Baum-Welch re-estimation | |
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| Re-estimation using the most likely state sequence | |
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| Initialization of Gaussian mixture distributions | |
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| Tied mixture distributions | |
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| Extension of stochastic models to word sequences | |
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| Implementing probability calculations | |
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| Using the Viterbi algorithm with probabilities in logarithmic form | |
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| Adding probabilities when they are in logarithmic form | |
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| Relationship between DTW and a simple HMM | |
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| State durational characteristics of HMMs | |
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| Chapter 9 summary | |
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| Chapter 9 exercises | |
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| Introduction to front-end analysis for automatic speech recognition | |
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| Introduction | |
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| Pre-emphasis | |
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| Frames and windowing | |
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| Filter banks, Fourier analysis and the mel scale | |
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| Cepstral analysis | |
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| Analysis based on linear prediction | |
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| Dynamic features | |
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| Capturing the perceptually relevant information | |
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| General feature transformations | |
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| Variable-frame-rate analysis | |
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| Chapter 10 summary | |
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| Chapter 10 exercises | |
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| Practical techniques for improving speech recognition performance | |
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| Introduction | |
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| Robustness to environment and channel effects | |
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| Feature-based techniques | |
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| Model-based techniques | |
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| Dealing with unknown or unpredictable noise corruption | |
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| Speaker-independent recognition | |
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| Speaker normalization | |
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| Model adaptation | |
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| Bayesian methods for training and adaptation of HMMs | |
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| Adaptation methods based on linear transforms | |
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| Discriminative training methods | |
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| Maximum mutual information training | |
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| Training criteria based on reducing recognition errors | |
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| Robustness of recognizers to vocabulary variation | |
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| Chapter 11 summary | |
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| Chapter 11 exercises | |
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| Automatic speech recognition for large vocabularies | |
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| Introduction | |
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| Historical perspective | |
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| Speech transcription and speech understanding | |
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| Speech transcription | |
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| Challenges posed by large vocabularies | |
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| Acoustic modelling | |
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| Context-dependent phone modelling | |
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| Training issues for context-dependent models | |
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| Parameter tying | |
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| Training procedure | |
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| Methods for clustering model parameters | |
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| Constructing phonetic decision trees | |
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| Extensions beyond triphone modelling | |
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| Language modelling | |
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| N-grams | |
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| Perplexity and evaluating language models | |
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| Data sparsity in language modelling | |
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| Discounting | |
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| Backing off in language modelling | |
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| Interpolation of language models | |
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| Choice of more general distribution for smoothing | |
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| Improving on simple N-grams | |
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| Decoding | |
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| Efficient one-pass Viterbi decoding for large vocabularies | |
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| Multiple-pass Viterbi decoding | |
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| Depth-first decoding | |
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| Evaluating LVCSR performance | |
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| Measuring errors | |
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| Controlling word insertion errors | |
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| Performance evaluations | |
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| Speech understanding | |
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| Measuring and evaluating speech understanding performance | |
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| Chapter 12 summary | |
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| Chapter 12 exercises | |
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| Neural networks for speech recognition | |
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| Introduction | |
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| The human brain | |
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| Connectionist models | |
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| Properties of ANNs | |
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| ANNs for speech recognition | |
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| Hybrid HMM/ANN methods | |
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| Chapter 13 summary | |
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| Chapter 13 exercises | |
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| Recognition of speaker characteristics | |
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| Characteristics of speakers | |
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| Verification versus identification | |
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| Assessing performance | |
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| Measures of verification performance | |
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| Speaker recognition | |
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| Text dependence | |
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| Methods for text-dependent/text-prompted speaker recognition | |
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| Methods for text-independent speaker recognition | |
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| Acoustic features for speaker recognition | |
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| Evaluations of speaker recognition performance | |
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| Language recognition | |
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| Techniques for language recognition | |
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| Acoustic features for language recognition | |
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| Chapter 14 summary | |
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| Chapter 14 exercises | |
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| Applications and performance of current technology | |
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| Introduction | |
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| Why use speech technology? | |
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| Speech synthesis technology | |
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| Examples of speech synthesis applications | |
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| Aids for the disabled | |
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| Spoken warning signals, instructions and user feedback | |
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| Education, toys and games | |
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| Telecommunications | |
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| Speech recognition technology | |
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| Characterizing speech recognizers and recognition tasks | |
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| Typical recognition performance for different tasks | |
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| Achieving success with ASR in an application | |
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| Examples of ASR applications | |
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| Command and control | |
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| Education, toys and games | |
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| Dictation | |
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| Data entry and retrieval | |
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| Telecommunications | |
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| Applications of speaker and language recognition | |
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| The future of speech technology applications | |
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| Chapter 15 summary | |
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| Chapter 15 exercises | |
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| Future research directions in speech synthesis and recognition | |
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| Introduction | |
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| Speech synthesis | |
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| Speech sound generation | |
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| Prosody generation and higher-level linguistic processing | |
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| Automatic speech recognition | |
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| Advantages of statistical pattern-matching methods | |
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| Limitations of HMMs for speech recognition | |
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| Developing improved recognition models | |
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| Relationship between synthesis and recognition | |
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| Automatic speech understanding | |
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| Chapter 16 summary | |
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| Chapter 16 exercises | |
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| Further Reading | |
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| Books | |
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| Journals | |
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| Conferences and workshops | |
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| The Internet | |
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| Reading for individual chapters | |
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| References | |
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| Solutions to Exercises | |
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| Glossary | |
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| Index | |