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 could 2007.
The 24 revised complete papers awarded have been rigorously reviewed and chosen from a variety of submissions for inclusion within the e-book. The papers are equipped in topical sections on nonlinear and nonconventional innovations, speech synthesis, speaker attractiveness, speech reputation, speech research, and exploitation of non-linear techniques.
Read 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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Additional info for Advances in Nonlinear Speech Processing: International Conference on Non-Linear Speech Processing, NOLISP 2007 Paris, France, May 22-25, 2007 Revised Selected Papers
It is shown within this chapter how similar techniques may be used in the framework of ﬁve diﬀerent applications. The experimental results of these applications are detailed and ﬁnally, conclusions and perspectives are given. 2 Features Extraction Audiovisual applications analyse video data and take beneﬁt from information extracted from the two available signals: the audio and the visual signals. Features extraction from these two signals is the preliminary step to any further analysis. The most common features used in the ﬁve applications mentionned previously are detailed here.
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. The ﬁnal classiﬁcation is based on the fusion of the outputs of both modalities. Multistream HMMs derive the most likely class by taking the product of the likelihoods of the two single-modality classiﬁer decisions, using appropriate weights λ.
Soc. Am. 101, 466–481 (1997) 17. : Spectral correlates of voice open quotient and glottal flow asymmetry: theory, limits and experimental data. In: Eurospeech 2001, Aalborg, Denmark (September 2001) 18. : On the use of the derivative of electroglottographic signals for characterization of nonpathological phonation. J. Acoust. Soc. Amer. 115(3), 1321–1332 (2004) 19. : Application of the wavelet transform for pitch detection of speech signals. IEEE trans. on IT 38(2), 917–924D (1992) 20. : Analysis, synthesis, and perception of voice quality variations among female and male talkers.