Advances in Nonlinear Speech Processing: International by Christophe d’Alessandro, Baris Bozkurt, Boris Doval, Thierry

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 overseas convention on Non-Linear Speech Processing, NOLISP 2007, held in Paris, France, in could 2007.

The 24 revised complete papers provided have been conscientiously reviewed and chosen from a number of submissions for inclusion within the publication. The papers are geared up in topical sections on nonlinear and nonconventional ideas, speech synthesis, speaker acceptance, speech popularity, speech research, and exploitation of non-linear techniques.

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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

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LDA . 100 40 1 Upsampling with Linear . . 1 . . . 40 LDA . . - . 40 40 40 15 Frames 600 "DCT" E[ . ] 1 ... 200 1 . . 4096 . Feature Normalization . 1 1 t 1 1 "DCT2" Rate = 100Hz Interpolation 200 Video Signal Rate = 25Hz "PRO" 1 t X-Profile 115 110 105 0 20 40 60 80 100 120 140 160 180 1 . 200 . 150 140 130 Y-Profile 120 110 100 . 90 80 0 20 40 60 80 100 120 140 160 180 200 400 Feature Mean + Normalization 1 1 1 . . . 400 LDA . 40 1 . . - 40 "PRO2" 1 . 130 125 120 . . 40 . 1 . .

The HTK Toolkit [34] software is used for model training and testing. For the feature fusion, the 39-dimensional audio vectors are simply concatenated with the 40-dimensional visual features (DCT or profiles respectively). The combined vectors are then LDA transformed before being used for model estimation. Decision fusion is obtained by combining separately trained models for the audio and the visual coefficients to two-stream models, with specific weights on each stream. We assume state-synchronous fusion for combining the stream likelihoods.

5. Visual speech features extraction. 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 [17]: majority voting, max, min, sum, .

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Advances in Nonlinear Speech Processing: International by Christophe d’Alessandro, Baris Bozkurt, Boris Doval, Thierry
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