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標(biāo)題: Titlebook: Linear Prediction of Speech; John D. Markel,Augustine H. Gray Book 1976 Springer-Verlag Berlin Heidelberg 1976 Automatische Spracherkennun [打印本頁]

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書目名稱Linear Prediction of Speech影響因子(影響力)




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John D. Markel,Augustine H. Gray Jr.nnées : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès.? ?L’intégration et la migration des bases de données: l’
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John D. Markel,Augustine H. Gray Jr.nnées : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès.? ?L’intégration et la migration des bases de données: l’
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John D. Markel,Augustine H. Gray Jr.nnées : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès.? ?L’intégration et la migration des bases de données: l’
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John D. Markel,Augustine H. Gray Jr.nnées : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès.? ?L’intégration et la migration des bases de données: l’
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John D. Markel,Augustine H. Gray Jr.nnées : la compilation, l’interprétation et l’optimisation des requêtes, l’environnement multiutilisateur, le concept de transaction et la sérialisation, les méthodes optimiste et pessimiste, les structures de stockage et les méthodes d’accès.? ?L’intégration et la migration des bases de données: l’
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0340-0034 sistent terminology. Topics are presented in such a manner that derivations and theoretical details are covered, along with Fortran sub- routines and practical considerations. Using this approach we hope to have made the material useful for a wide range of backgrounds and interests.978-3-642-66288-1978-3-642-66286-7Series ISSN 0340-0034
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ion farmers in the country spread across ten states are engaged in cotton production and grow cotton on an area of about 11 million hectares. The major cotton producing states in the country are Maharashtra, Gujarat, Andhra Pradesh, Punjab and Tamil Nadu. Despite being one of the top most cotton cou
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John D. Markel,Augustine H. Gray Jr.r la modélisation des données, les langages de base de données, l‘a(chǎn)rchitecture des systèmes et l‘évolution post-relationnelle. ..- Notions fondamentales: le modèle relationnel, les composants d’un système de gestion de bases de données, l’organisation de la mise en ?uvre d’une base de données, les t
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John D. Markel,Augustine H. Gray Jr.isation des données, les langages de base de données, l‘a(chǎn)rchitecture des systèmes et l‘évolution post-relationnelle. ..- Notions fondamentales: le modèle relationnel, les composants d’un système de gestion de bases de données, l’organisation de la mise en ?uvre d’une base de données, les taches de g
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Introduction,ty that no single model has been developed which can account for all of the observed characteristics in human speech (nor would one probably desire such a model because of its inevitable complexity). A basic criterion of modeling is to find mathematical relations which can be used to represent a lim
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Formulations,968, Atal and Schroeder [1967, 1968 b, 1968 c] published results on predictive coding of speech signals. Itakura and Saito also published a condensed English version of the maximum likelihood estimation procedure in 1968 and a detailed presentation in 1970. In 1970, Atal [1970 a] presented the first
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Acoustic Tube Modeling,1960; Kelly and Lochbaum, 1962]. In these studies, it was shown that from a given tube shape, the resonance frequencies could be obtained. The important inverse problem of determining a unique tube shape from resonance characteristics has received considerable attention in the studies by Schroeder a
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Spectral Analysis,ous methods have been proposed for representing the spectral characteristics of speech, such as bandpass filter sampling and analysis-by-synthesis, there are a number of reasons why linear prediction techniques are becoming widely used.
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Automatic Formant Trajectory Estimation, acoustical speech parameters (in the sense that they are relatable to the physical production of speech and are parameters of the analog vocal tract model) are the formant and fundamental frequency parameters.
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Fundamental Frequency Estimation,lly considered to be one of the acoustical correlates to the perceived intonation pattern of speech. If the fundamental frequency of a speaker is constant, the speech would be perceived as being machine-like or monotone. If the speaker is excited, the fundamental frequency generally increases. It is
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Computational Considerations in Analysis,thus far, an implicit assumption was made that the computation occurred without error. For example, the norm squares . and . of Chapter 3 are theoretically non-negative, yet when they are evaluated using the computer programs presented they may yield negative results due to computer errors. Numerica
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Vocoders, the synthesis quality of devices that code speech for efficient transmission. An excellent summary of that effort up to 1966 is presented by Schroeder [1966]. One of the major problems was the difficulty in obtaining accurate separation of the driving function behavior (fundamental frequency) from
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Further Topics,The purposes of this final chapter are to briefly describe some aplications of linear prediction other than those previously discussed, to summarize a few impornt properties of linear prediction, and finally to present several thoughts about further research directions.
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https://doi.org/10.1007/978-3-642-66286-7Automatische Spracherkennung; Pr?diktion; Speech; Sprachsynthese; stability; structure; synthesis
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Fundamental Frequency Estimation,quency will be measured. In the linear speech production model, the fundamental frequency is the rate at which glottal volume velocity pulses are applied to the vocal tract, i.e., the driving function to the model is periodic with a period of 1/...
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Introduction,ited physical situation with a minimum of complexity and a maximum of accuracy. One of the most successful models of acoustical speech behavior is the . developed by Fant [1960]. This model will be referred to throughout as the speech production model.
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