Titlebook: Nonlinear Speech Modeling and Applications; Advanced Lectures an Gérard Chollet,Anna Esposito,Maria Marinaro Conference proceedings 2005 Sp

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Some Notes on Nonlinearities of Speechty is not easily handled from an engineering and mathematical point of view. This paper is an attempt to make accessible to untrained people the notion of nonlinearity in speech, revising several nonlinear speech phenomena and the engineering endeavour for modeling them.

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Nonlinear Speech Processing: Overview and Possibilities in Speech Codinged, such as multi-start random weights initialization, regularization, early stop with validation, committee of neural nets, different architectures, etc. Although the paper is devoted to ADPCM speech coding (scalar and vectorial schemes), this study offers a good chance to deal with nonlinear predi

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The Analysis of Voice Quality in Speech Processingal features and running continuously through the individual’s speech. The distinctive tone of speech sounds produced by a particular person yields a particular voice. Voice quality is at the centre of several speech processing issues. In speech recognition, voice differences, particularly extreme di

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Identification of Nonlinear Oscillator Models for Speech Analysis and Synthesisand Kleijn, 1994). Since then, numerous developments have been initiated to turn nonlinear oscillators into a standard tool for speech technology. The present contribution will review and compare several of these attempts with a special emphasis on adaptive model identification from data and the app

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Speech Modelling Based on Acoustic-to-Articulatory Mappingh a set of observed formant frequencies. The article presents an analytical method for acoustic-to-articulatory mapping, which is generic. The plausibility of computed vocal tract shapes is examined and methodological problems as well as possible applications are considered.

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鎮(zhèn)壓

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Predictive Connectionist Approach to Speech Recognitionbasic building element, the context-dependent HCNN model, is connectionist network trained to capture dynamics of sub-word units of speech. The described HCNN model belongs to a family of Hidden Markov Model/Multi-Layer Perceptron (HMM/MLP) hybrids, usually referred to as Predictive Neural Networks

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