By Christophe d’Alessandro, Baris Bozkurt, Boris Doval, Thierry Dutoit, Nathalie Henrich (auth.), Mohamed Chetouani, Amir Hussain, Bruno Gas, Maurice Milgram, Jean-Luc Zarader (eds.)
This publication constitutes the completely refereed postproceedings of the foreign convention on Non-Linear Speech Processing, NOLISP 2007, held in Paris, France, in may well 2007.
The 24 revised complete papers provided have been conscientiously reviewed and chosen from various submissions for inclusion within the e-book. The papers are equipped in topical sections on nonlinear and nonconventional strategies, speech synthesis, speaker reputation, speech popularity, speech research, and exploitation of non-linear techniques.
Read Online or Download Advances in Nonlinear Speech Processing: International Conference on Non-Linear Speech Processing, NOLISP 2007 Paris, France, May 22-25, 2007 Revised Selected Papers PDF
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Extra resources for Advances in Nonlinear Speech Processing: International Conference on Non-Linear Speech Processing, NOLISP 2007 Paris, France, May 22-25, 2007 Revised Selected Papers
28 low spatial frequency DCT coeﬃcients are extracted, in a zigzag manner. Two main approaches may then be adopted. The ﬁrst one is called early fusion and is based on the computation of audiovisual features vectors from audio and visual features vectors (for instance concatenation). The second one is called late fusion and relies on the fusion at the decision level. Many diﬀerent methods may be applied to combine the outputs of all the classiﬁers used in the modeling process : majority voting, max, min, sum, .
All these probabilies are thus required to compute the sequence of hidden states. The very ﬁrst stage is then to estimate them using the Baum-Welch algorithm over a training set. 3 HMMs Extensions Two other kinds of statistical models may be derived from the classical HMMs to facilitate audiovisual process modeling, namely the Multistream HMMs and the coupled HMMs (CHMMs) [22,23,24]. Multistream HMMs may be considered as a late fusion method. In this approach, each modality (here the audio one and the visual one ) is independently processed and pre-classiﬁed.
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).