By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)
This quantity includes the lawsuits of NOLISP 2009, an ISCA instructional 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 notion of NOLISP workshops is to give and speak about new rules, concepts and effects regarding substitute ways in speech processing which may leave from the mainstream. for you to paintings on the front-end of the topic region, 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 reliant part research five. Nearest pals 6. Neural networks 7. determination timber eight. Non-parametric types 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 college of Vic (UVic) got here from the UVic learn team on sign Processing and was once supported by means of the Hardware-Software learn crew. we want to recognize the ?nancial aid received from the M- istry of technological know-how 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 approach sooner than their popularity for the workshop and have been revised after being offered at NOLISP 2009.
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Extra resources for Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers
As a consequence, handling high dimensional feature vectors may be diﬃcult if not enough learning data are available. Dimension reduction may then be used to overcome this issue. Many methods are available: Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA), . . PCA has already been presented within the section concerning eigenfaces. LDA is much more appropriate for classiﬁcation. 2) since classes are then known (phones). On the diﬀerence of PCA which tends only to maximize intra-classes scattering, LDA also tends in the same time to minimize interclasses scattering.
The line with the highest cumulated amplitude is then chosen for representing the period. The GCI is then computed as the instant where the selected line starts at the smallest scale (highest frequency) (see Figure 21, right panel). 3 Comparison with Electroglottography For GCI detection algorithm evaluation, a reference is needed. The electroglottographic (EGG) reference is chosen, because it is an accurate and non-invasive GCI measurement method. A database of speech including various productions like vocal fry, modal and falsetto voices, spontaneous and read speech, male and female voices, has been recorded.
It is also possible to process the concatenated vectors with any transformation (such as Linear Discriminant Analysis LDA) in order to reduce the increased number of coeﬃcients and facilitate classiﬁcation (see Figure 7). Late fusion: as already explained, multistream HMMs derives the most likely speech class by taking the product of the likelihoods produced using models learned for each mode. The ﬁnal likelihood is then: P (oav,t |s) = P (oa,t |s)λa P (ov,t |s)λv Experiments: Data and features.