By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)
This quantity includes the court cases of NOLISP 2009, an ISCA educational and Workshop on Non-Linear Speech Processing held on the college 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 give and speak about new principles, recommendations and effects regarding substitute techniques in speech processing which can leave from the mainstream. so one can paintings on the front-end of the topic zone, the next 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 facts four. self sufficient part research five. Nearest friends 6. Neural networks 7. selection timber eight. Non-parametric versions nine. Dynamics for non-linear platforms 10. Fractal tools 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 team on sign Processing and used to be supported through the Hardware-Software learn crew. we want to recognize the ?nancial help acquired from the M- istry of technological know-how and Innovation of Spain (MICINN), collage of Vic, ISCA, and EURASIP. All contributions to this quantity are unique. They have been topic to a doub- blind refereeing process prior to their popularity for the workshop and have been revised after being offered at NOLISP 2009.
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Additional info for Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers
The last step is the re-synthesis of the signal in order to reconstruct the speech segment of the source voice after the conversion. The most representative techniques of voice conversion are based on vector quantization , on Gaussian Mixture Models and derived [45,46,47,48], on Some Experiments in Audio-Visual Speech Processing 49 Multiple Linear Regression  and on an indexation in a client memory . Two of these conversion methods will be developed afterwards and their inﬂuence on an automatic speaker recognition system will be evaluated.
An example of the resulting acquired picture is shown in ﬁgure 12. Therefore we introduced a new biometric modality based on a clientdependent measure of the synchrony between acoustic and visual speech features. Some Experiments in Audio-Visual Speech Processing 47 Fig. 12. 6. In order to equalize the sample rates of acoustic and visual features (initially 100 Hz and 25 Hz respectively), visual features are linearly interpolated. 8) is applied in order to compute the clientdependent synchrony model (A, B).
All these decision processes may take beneﬁt from considering in the same time audio features vectors and visual ones. Some Experiments in Audio-Visual Speech Processing 35 Fig. 4. Visual speech features extraction. 1– Eyes detection. 2– Selection of the region of interest where to look for the mouth. 3– Mouth detection. 4-5– DCT coeﬃcients computation. Fig. 5. Visual speech features extraction. 28 low spatial frequency DCT coeﬃcients are extracted, in a zigzag manner. Two main approaches may then be adopted.