05648nam 22007094a 450 991014374150332120200721114210.01-280-27512-X97866102751200-470-02091-10-470-02090-3(CKB)1000000000356175(EBL)232683(OCoLC)70110497(SSID)ssj0000199141(PQKBManifestationID)11188037(PQKBTitleCode)TC0000199141(PQKBWorkID)10184482(PQKB)10691831(MiAaPQ)EBC232683(EXLCZ)99100000000035617520041116d2005 uy 0engur|n|---|||||txtccrMathematical models for speech technology[electronic resource] /Stephen E. LevinsonChichester, West Sussex, England ;Hoboken, NJ, USA John Wileyc20051 online resource (283 p.)Description based upon print version of record.0-470-84407-8 Includes bibliographical references (p. [243]-255) and index.Mathematical Models for Speech Technology; Contents; Preface; 1 Introduction; 1.1 Milestones in the history of speech technology; 1.2 Prospects for the future; 1.3 Technical synopsis; 2 Preliminaries; 2.1 The physics of speech production; 2.1.1 The human vocal apparatus; 2.1.2 Boundary conditions; 2.1.3 Non-stationarity; 2.1.4 Fluid dynamical effects; 2.2 The source-filter model; 2.3 Information-bearing features of the speech signal; 2.3.1 Fourier methods; 2.3.2 Linear prediction and the Webster equation; 2.4 Time-frequency representations; 2.5 Classification of acoustic patterns in speech2.5.1 Statistical decision theory2.5.2 Estimation of class-conditional probability density functions; 2.5.3 Information-preserving transformations; 2.5.4 Unsupervised density estimation - quantization; 2.5.5 A note on connectionism; 2.6 Temporal invariance and stationarity; 2.6.1 A variational problem; 2.6.2 A solution by dynamic programming; 2.7 Taxonomy of linguistic structure; 2.7.1 Acoustic phonetics, phonology, and phonotactics; 2.7.2 Morphology and lexical structure; 2.7.3 Prosody, syntax, and semantics; 2.7.4 Pragmatics and dialog; 3 Mathematical models of linguistic structure3.1 Probabilistic functions of a discrete Markov process3.1.1 The discrete observation hidden Markov model; 3.1.2 The continuous observation case; 3.1.3 The autoregressive observation case; 3.1.4 The semi-Markov process and correlated observations; 3.1.5 The non-stationary observation case; 3.1.6 Parameter estimation via the EM algorithm; 3.1.7 The Cave-Neuwirth and Poritz results; 3.2 Formal grammars and abstract automata; 3.2.1 The Chomsky hierarchy; 3.2.2 Stochastic grammars; 3.2.3 Equivalence of regular stochastic grammars and discrete HMMs; 3.2.4 Recognition of well-formed strings3.2.5 Representation of phonology and syntax4 Syntactic analysis; 4.1 Deterministic parsing algorithms; 4.1.1 The Dijkstra algorithm for regular languages; 4.1.2 The Cocke-Kasami-Younger algorithm for context-free languages; 4.2 Probabilistic parsing algorithms; 4.2.1 Using the Baum algorithm to parse regular languages; 4.2.2 Dynamic programming methods; 4.2.3 Probabilistic Cocke-Kasami-Younger methods; 4.2.4 Asynchronous methods; 4.3 Parsing natural language; 4.3.1 The right-linear case; 4.3.2 The Markovian case; 4.3.3 The context-free case; 5 Grammatical Inference5.1 Exact inference and Gold's theorem5.2 Baum's algorithm for regular grammars; 5.3 Event counting in parse trees; 5.4 Baker's algorithm for context-free grammars; 6 Information-theoretic analysis of speech communication; 6.1 The Miller et al. experiments; 6.2 Entropy of an information source; 6.2.1 Entropy of deterministic formal languages; 6.2.2 Entropy of languages generated by stochastic grammars; 6.2.3 Epsilon representations of deterministic languages; 6.3 Recognition error rates and entropy; 6.3.1 Analytic results derived from the Fano bound; 6.3.2 Experimental results7 Automatic speech recognition and constructive theories of languageMathematical Models of Spoken Language presents the motivations for, intuitions behind, and basic mathematical models of natural spoken language communication. A comprehensive overview is given of all aspects of the problem from the physics of speech production through the hierarchy of linguistic structure and ending with some observations on language and mind. The author comprehensively explores the argument that these modern technologies are actually the most extensive compilations of linguistic knowledge available.Throughout the book, the emphasis is on placing all the material inSpeech processing systemsComputational linguisticsApplied linguisticsMathematicsStochastic processesKnowledge, Theory ofElectronic books.Speech processing systems.Computational linguistics.Applied linguisticsMathematics.Stochastic processes.Knowledge, Theory of.006.4/54/015118006.454015118410.15118Levinson Stephen E157418MiAaPQMiAaPQMiAaPQBOOK9910143741503321Mathematical models for speech technology2163512UNINA01610nam 2200493 450 991082918890332120220819063705.0(CKB)3780000000390620(MiAaPQ)EBC4908286(RPAM)19272269(PPN)228541972(EXLCZ)99378000000039062020170808h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierCategorification and higher representation theory /Anna Beliakova, Aaron Lauda, editorsProvidence, Rhode Island :American Mathematical Society,2017.©20171 online resource (376 pages) illustrationsContemporary mathematics,6830271-41321-4704-2460-6 1-4704-3689-2 Includes bibliographical references at the end of each chapters.Contemporary mathematics (American Mathematical Society).6830271-4132Categories (Mathematics)Mathematical analysisAlgebraCategories (Mathematics)Mathematical analysis.Algebra.512.6281R5017B1020C0814F0518D1017B5017B5517B67mscBeliakova Anna1968-Lauda Aaron1981-MiAaPQMiAaPQMiAaPQBOOK9910829188903321Categorification and higher representation theory3983094UNINA