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| Introduction | |
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| Motivations | |
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| Spoken Language System Architecture | |
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| Book Organization | |
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| Target Audiences | |
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| I | |
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| Spoken language Structure | |
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| Sound and Human Speech Systems | |
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| Phonetics and Phonology | |
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| Syllables and Words | |
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| Syntax and Semantics | |
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| Probability, Statistics, and Information Theory | |
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| Probability Theory | |
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| Estimation Theory | |
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| Significance Testing | |
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| Information Theory | |
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| Pattern Recognition | |
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| Bayes' Decision Theory | |
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| How to Construct Classifiers | |
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| Discriminative Training | |
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| Unsupervised Estimation Methods | |
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| Classification and Regression Trees | |
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| Speech Processing | |
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| Digital Signal Processing | |
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| Digital Signals and Systems | |
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| Continuous-Frequency Transforms | |
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| Discrete-Frequency Transforms | |
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| Digital Filters and Windows | |
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| Digital Processing of Analog Signals | |
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| Multirate Signal Processing | |
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| Filterbanks | |
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| Stochastic Processes | |
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| Speech Signal Representations | |
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| Short-Time Fourier Analysis | |
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| Acoustical Model of Speech Production | |
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| Linear Predictive Coding | |
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| Cepstral Processing | |
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| Perceptually Motivated Representations | |
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| Formant Frequencies | |
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| The Role of Pitch | |
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| Speech Coding | |
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| Speech Coders Attributes | |
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| Scalar Waveform Coders | |
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| Scalar Frequency Domain Coders | |
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| Code Excited Linear Prediction (CELP) | |
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| Low-Brit Speech Coders | |
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| III | |
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| Hidden Markov Models | |
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| The Markov Chain | |
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| Definition of the Hidden Markov Model | |
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| Continuous and Semicontinuous HMMs | |
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| Practical Issues in Using HMMs | |
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| HMM Limitations | |
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| Acoustic Modeling | |
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| Variability in the Speech Signal | |
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| How to Measure Speech Recognition Errors | |
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| Signal Processing-Extracting Features | |
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| Phonectic Modeling-Selecting Appropriate Units | |
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| Acoustic Modeling-Scoring Acoustic Features | |
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| Adaptive Techniques-Minimizing Mismatches | |
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| Confidence Measures: Measuring the Reliability | |
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| Other Techniques | |
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| Case Study: Whisper | |
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| Environmental Robustness | |
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| The Acoustical Environment | |
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| Acoustical Transducers | |
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| Adaptive Echo Cancellation (AEC) | |
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| Multimicrophone Speech Enhancement | |
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| Environment Compensation Preprocessing | |
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| Environment Model Adaptation | |
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| Modeling Nonstationary Noise | |
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| Language Modeling | |
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| Formal Language Theory | |
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| Stochastic Language Models | |
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| Complexity Measure of Language Models | |
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| N-Gram Smoothing | |
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| Adaptive Language Models | |
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| Practical Issues | |
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| Basic Search Algorithms | |
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| Basic Search Algorithms | |
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| Search Algorithms for Speech Recognition | |
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| Language Model States | |
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| Time-Synchronous Viterbi Beam Search | |
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| Stack Decoding (A Search) | |
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| Large-Vocabulary Search Algorithms | |
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| Efficient Manipulation of a Tree Lexicon | |
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| Other Efficient Search Techniques | |
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| N-Best and Multipass Search Strategies | |
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| Search-Algorithm Evaluation | |
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| Case Study-Microsoft Whisper | |
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| IV | |
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| Text and Phonetic Analysis | |
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| Modules and Data Flow | |
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| Lexicon | |
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| Document Structured Detection | |
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| Text Normalization | |
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| Linguistic Analysis | |
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| Homograph Disambiguation | |
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| Morphological Analysis | |
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| Letter-to-Sound | |