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April 20, 2026, 3:51 p.m.
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| Hori T. Speech Recognition Algorithms Using Weighted Finite-State Transduce.2013.pdf | 1.4 MB |
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SOURCE: Hori T. Speech Recognition Algorithms Using Weighted Finite-State Transduce.2013
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MEDIAINFO
Textbook in PDF format This book introduces the theory, algorithms, and implementation techniques for efficient decoding in speech recognition mainly focusing on the Weighted Finite-State Transducer (WFST) approach. The decoding process for speech recognition is viewed as a search problem whose goal is to find a sequence of words that best matches an input speech signal. Since this process becomes computationally more expensive as the system vocabulary size increases, research has long been devoted to reducing the computational cost. Recently, the WFST approach has become an important state-of-the-art speech recognition technology, because it offers improved decoding speed with fewer recognition errors compared with conventional methods. However, it is not easy to understand all the algorithms used in this framework, and they are still in a black box for many people. In this book, we review the WFST approach and aim to provide comprehensive interpretations of WFST operations and decoding algorithms to help anyone who wants to understand, develop, and study WFST-based speech recognizers. We also mention recent advances in this framework and its applications to spoken language processing. Table of Contents: Introduction / Brief Overview of Speech Recognition / Introduction to Weighted Finite-State Transducers / Speech Recognition by Weighted Finite-State Transducers / Dynamic Decoders with On-the-fly WFST Operations / Summary and Perspective Preface Introduction Speech Recognition and Computation Why WFST? Purpose of this Book Book Organization Brief Overview of Speech Recognition Statistical Framework of Speech Recognition Speech Analysis Acoustic Model Hidden Markov Model Computation of Acoustic Likelihood Output Probability Distribution Subword Models and Pronunciation Lexicon Context-dependent Phone Models Language Model Finite-State Grammar N-gram Model Back-off Smoothing Decoder Viterbi Algorithm for Continuous Speech Recognition Time-Synchronous Viterbi Beam Search Practical Techniques for LVCSR Context-dependent Phone Search Network Lattice Generation and N-Best Search Introduction to Weighted Finite-State Transducers Finite Automata Basic Properties of Finite Automata Semiring Basic Operations Transducer Composition Optimization Determinization Weight Pushing Minimization Epsilon Removal Speech Recognition by Weighted Finite-State Transducers Overview of WFST-based Speech Recognition Construction of Component WFSTs Acoustic Models Phone Context Dependency Pronunciation Lexicon Language Models Composition and Optimization Decoding Algorithm Using a Single WFST Decoding Performance Dynamic Decoders with On-the-fly WFST Operations Problems in the Native WFST Approach On-the-fly Composition and Optimization Known Problems of On-the-fly Composition Approach Look-ahead Composition How to Obtain Prospective Output Labels Basic Principle of Look-ahead Composition Realization of Look-ahead Composition Using a Filter Transducer Look-ahead Composition with Weight Pushing Generalized Composition Interval Representation of Label Sets On-the-fly Rescoring Approach Construction of Component WFSTs for On-the-fly Rescoring Concept Algorithm Approximation in Decoding Comparison with Look-ahead Composition Summary and Perspective Realization of Advanced Speech Recognition Techniques Using WFSTs WFSTs for Extended Language Models Dynamic Grammars Based on WFSTs Wide-context-dependent HMMs Extension of WFSTs for Multi-modal Inputs Use of WFSTs for Learning Integration of Speech and Language Processing Other Speech Applications Using WFSTs Conclusion Bibliography Authors' Biographies
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