Toward invariant functional representations of variable surface fundamental frequency contours: Synthesizing speech melody via model-based stochastic learning

This paper describes the application of the analysis by synthesis paradigm to the melody of speech. Acomplete chain of processes is described from the acoustic analysis of fundamental frequency (f0), via thephonetic modelling of f0 using the Momel algorithm, to the surface phonological representation of thecurves using the INTSINT alphabet. Each step of the chain is designed as a reversible process which can beused to generate an acoustic output allowing an objective evaluation of the analysis. Finally, the currentimplementation of ProZed, a prosody editor for linguists, is described. It is argued that an explicit set ofmodelling tools like this will allow linguists to test different models of phonological structure which, it ishoped, will result in the availability of more and better data on a wide variety of languages.

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A new control model based on rising and falling fundamental frequency

The Journal of the Acoustical Society of America ◽

10.1121/1.416640 ◽

1996 ◽

Vol 100 (4) ◽

pp. 2824-2824 ◽

Cited By ~ 2

Author(s):

Takaaki Moriyama ◽

Hitoshi Ogawa ◽

Seiichi Tenpaku

Keyword(s):

Fundamental Frequency ◽

Control Model ◽

Model Based

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Applying generation process model constraint to fundamental frequency contours generated by hidden-Markov-model-based speech synthesis

Acoustical Science and Technology ◽

10.1250/ast.33.221 ◽

2012 ◽

Vol 33 (4) ◽

pp. 221-228 ◽

Cited By ~ 5

Author(s):

Tetsuya Matsuda ◽

Keikichi Hirose ◽

Nobuaki Minematsu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Fundamental Frequency ◽

Speech Synthesis ◽

Process Model ◽

Hidden Markov ◽

Generation Process ◽

Model Based ◽

Model Constraint

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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