By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)
This quantity comprises the lawsuits 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 offer and talk about new rules, thoughts and effects with regards to substitute ways in speech processing that can leave from the mainstream. so one can paintings on the front-end of the topic quarter, 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 information four. self sustaining part research five. Nearest buddies 6. Neural networks 7. choice bushes eight. Non-parametric versions nine. Dynamics for non-linear structures 10. Fractal equipment eleven. Chaos modeling 12. Non-linear di?erential equations The initiative to arrange NOLISP 2009 on the collage of Vic (UVic) got here from the UVic examine crew on sign Processing and was once supported by way of the Hardware-Software study team. we wish to recognize the ?nancial aid acquired 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 process ahead of their recognition 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
5. 23 The mean difference between both sets of GCI is computed (Dm) The procedure is repeated, varying Td. The optimal delay Do between the DEGG and the speech signal is obtained for the value of Td corresponding to minimum Dm. The results of this analysis are summarized in Table 1 for 8 sustained vowels: Table 1. 0 908 Where Tr represents the sentence duration, Ndegg is the number of GCIs detected on the DEGG signal, Nspeech is the number of GCIs detected using wavelets, and %diff the percentage of difference between the two measures.
103, 566–571 (1998) 27. : Robust Glottal closing Detection using the Wavelet Transform. In: Proceedings of Eurospeech 1999 Budapest, Hungary, vol. 6, pp. 2805–2808 (1999) 28. : Glottal closing Detection using EGG and the Wavelet Transform. In: Advances in Quantitative Laryngoscopy, Voice and Speech Research Proceedings of the 4th International Workshop, Jena, Germany, pp. 147–154 (2000) 29. : Significance of group delay functions in spectrum estimation. IEEE Trans. Signal Process. 40(9) (1992) Some Experiments in Audio-Visual Speech Processing G.
To overcome the lack of training data dedicated to each speaker, adaptation of a world (or universal) model is performed using the MAP algorithm. The veriﬁcation score is computed as the following likelihood ratio : S(V , V λ ) = 1 Nx log x P (x |λ) P (x |Ω) where x denotes an observation vector in the audio stream of V , Ω the world model and Nx the number of observation vectors considered in the whole speech sequence. Synchrony modality Speaker conversion and face animation can be considered as high-eﬀort forgeries, which – if they are performed correctly – are very diﬃcult to detect.
Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers by Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)