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By Jordi Sole-Casals, Vladimir Zaiats

This ebook constitutes the court cases of the overseas convention on Advances in Nonlinear Speech Processing, NOLISP 2009, held in Vic (Barcelona), Spain in the course of June 25-27, 2009. the quantity bargains with new rules, suggestions and effects concerning replacement methods in speech processing.

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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, 2009, Revised Selected

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Both, and G. 5. 2 Normalized RMS Amplitude (NRMS) In irregularly phonated speech, there are generally fewer glottal pulses in a given time frame than in regularly phonated speech. This can be captured by the RMS amplitude (intensity), normalized by the RMS amplitude of a longer interval. Surana and Slifka [1] divided the RMS intensity of each frame by the intensity calculated over the entire sentence and then took the mean of these values in order to compute the NRMS cue for the vowel. If the intensity level changes along the sentence, then normalizing with the sentence RMS can become misleading.

Panels (a) to (d): for the corresponding window location, two cycles of the glottal source estimation achieved by the traditional ZZT (dotted line) and by the proposed ZCZT-based technique (solid line). proposed ZCZT-based technique gives a reliable estimation of the glottal source on a large segment around the GCI. Besides the obtained performance is comparable to what is carried out by the ideal ZCZT. In Figure 5 the glottal source estimated by the traditional ZZT and the proposed ZCZT-based method are displayed for four different positions of the window (for the vowel /a/ from the same file).

For the recording of the corpus, a female professional speaker was chosen as it was thought she could successfully convey the suitable expressive style to each text category (simulated/acted speech). For the design of texts semantically related to different expressive styles, an existing textual database of advertisements extracted from newspapers and magazines was used. Based on audio-visual publicity, three categories of the textual corpus were chosen and the most suitable emotion/style were assigned to them: New technologies (neutral), education (happy) and trips (sad-melancholic).

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