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Titlebook: Nonlinear Analyses and Algorithms for Speech Processing; International Confer Marcos Faundez-Zanuy,Léonard Janer,Virginia Espino Conference

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发表于 2025-3-21 18:59:52 | 显示全部楼层 |阅读模式
书目名称Nonlinear Analyses and Algorithms for Speech Processing
副标题International Confer
编辑Marcos Faundez-Zanuy,Léonard Janer,Virginia Espino
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Nonlinear Analyses and Algorithms for Speech Processing; International Confer Marcos Faundez-Zanuy,Léonard Janer,Virginia Espino Conference
描述We present in this volume the collection of ?nally accepted papers of NOLISP 2005 conference. It has been the third event in a series of events related to N- linear speech processing, in the framework of the European COST action 277 “Nonlinear speech processing”. Many speci?cs of the speech signal are not well addressed by conv- tional models currently used in the ?eld of speech processing. The purpose of NOLISP is to present and discuss novel ideas, work and results related to alternative techniques for speech processing, which depart from mainstream approaches. With this intention in mind, we provide an open forum for discussion. Alt- nate approaches are appreciated, although the results achieved at present may not clearly surpass results based on state-of-the-art methods. The call for papers was launched at the beginning of 2005, addressing the following domains: 1. Non-Linear Approximation and Estimation 2. Non-Linear Oscillators and Predictors 3. Higher-Order Statistics 4. Independent Component Analysis 5. Nearest Neighbors 6. Neural Networks 7. Decision Trees 8. Non-Parametric Models 9. Dynamics of Non-Linear Systems 10. Fractal Methods 11. Chaos Modeling 12. Non-Linear Di?er
出版日期Conference proceedings 2005
关键词ai; algorithms; artificial intelligence; higher-order statistics; image processing; intelligent informati
版次1
doihttps://doi.org/10.1007/11613107
isbn_softcover978-3-540-31257-4
isbn_ebook978-3-540-32586-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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Exploiting High-Level Information Provided by ALISP in Speaker Recognition-based Gaussian Mixture Models (GMM) system exploiting the speaker discriminating properties of individual speech classes. The resulting fused system reduced the error rate over the individual systems on the NIST 2004 Speaker Recognition Evaluation data.
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Cepstrum-Based Estimation of the Harmonics-to-Noise Ratio for Synthesized and Human Voice Signalsrce influence is removed using a novel harmonic pre-emphasis technique. The results indicate accurate HNR estimation using the present approach. A preliminary investigation of the method with a set of normal/ pathological data is investigated.
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Optimal Size of Time Window in Nonlinear Features for Voice Quality Measurement, one of them including these features, have been implemented with the purpose of validating the usefulness of the suggested nonlinear features. We obtain slight improvements with respect to a classical system.
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Support Vector Machines Applied to the Detection of Voice Disorderssuch a Gaussian Mixture or Hidden Markov Models): the SVM models the boundary between the classes instead of modelling the probability density of each class. In this paper it is shown that the scheme proposed fed with short-term cepstral and noise parameters can be applied for the detection of voice impairments with a good performance.
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0302-9743 nts related to N- linear speech processing, in the framework of the European COST action 277 “Nonlinear speech processing”. Many speci?cs of the speech signal are not well addressed by conv- tional models currently used in the ?eld of speech processing. The purpose of NOLISP is to present and discus
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