By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)
This quantity comprises the complaints of NOLISP 2009, an ISCA instructional and Workshop on Non-Linear Speech Processing held on the collage of Vic (- talonia, Spain) in the course of June 25-27, 2009. NOLISP2009wasprecededbythreeeditionsofthisbiannualeventheld2003 in Le Croisic (France), 2005 in Barcelona, and 2007 in Paris. the most concept of NOLISP workshops is to provide and talk about new principles, suggestions and effects concerning replacement ways in speech processing which can leave from the mainstream. which will paintings on the front-end of the topic zone, the subsequent domain names of curiosity were de?ned for NOLISP 2009: 1. Non-linear approximation and estimation 2. Non-linear oscillators and predictors three. Higher-order records four. self sufficient part research five. Nearest buddies 6. Neural networks 7. choice bushes eight. Non-parametric versions nine. Dynamics for non-linear platforms 10. Fractal equipment eleven. Chaos modeling 12. Non-linear di?erential equations The initiative to prepare NOLISP 2009 on the collage of Vic (UVic) got here from the UVic learn staff on sign Processing and used to be supported by means of the Hardware-Software learn staff. we want to recognize the ?nancial help received from the M- istry of technology and Innovation of Spain (MICINN), college of Vic, ISCA, and EURASIP. All contributions to this quantity are unique. They have been topic to a doub- blind refereeing method sooner than their attractiveness for the workshop and have been revised after being awarded at NOLISP 2009.
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Additional resources for Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers
These models are at the heart of many audiovisual applications and are detailed in this section. 1 Gaussian Mixture Models As already mentionned, GMM distribution is a mixture whose components are classical Gaussian distributions. e. its probability of appearance), the mean vector and the covariance matrix of this k th Gaussian component. , X T ) = p(X t ) (4) T This supposes that the speech feature vectors are independent given the GMM model. Therefore the same likelihood will be obtained if we take a random order of the same sequence of T vectors.
A ﬁrst extension was deﬁned in  to take local descriptions around keypoints into account. SVD is then performed on the matrix G deﬁned as Gij = f (Cij )g(Rij ) where Cij denotes the correlation between gray-levels around i and j keypoints, and where g is the gaussian function previously deﬁned. Two diﬀerent f functions may be used : Exponential: f (Cij ) = exp(−(Cij − 1)2 /2γ 2 ) . Linear: f (Cij ) = (Cij + 1)/2 . 4 . A second improvement has been experimented in  where gray-level correlation is replaced with SIFT descriptors correlation (only the linear form for f function is tested).
State state . LDA 40 . 79 . 40 Decision Fusion Feature Fusion Video FrontEnd Fig. 7. , x200 Fig. 8. 3). Horizontal and vertical projection proﬁles are then computed (cf ﬁg. 8). The 200 projections along the X axis and 200 projections along the Y axis are concatenated to a single vector whose dimension is reduced to 40 using an LDA after a feature mean normalization. So as to capture dynamic speech information, each vector is then extended by concatenating its 7 chronologically preceding and the 7 following vectors.