Figure 3 Frame blocking of the speech signal-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
| Main Authors: | S. Hamidreza Kasaei, S. Mohammadreza Kasaei, S. Alireza Kasaei |
|---|---|
| Format: | info Image |
| Bahasa: | eng |
| Terbitan: |
, 2011
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| Subjects: | |
| Online Access: |
https://www.edusoft.ro/brain/index.php/brain/issue/view/14 |
Daftar Isi:
- The next frame will begin M samples (i.e. 156 samples) after the first frame, and it will overlap the first frame by N-M samples (256 – 156 = 100 samples). Then the third frame will start at 2M samples after the first frame and it will overlap first frame by N-2M. The fourth frame will start at 3M samples after the first, and it will overlap it by N-3M. The process will continue until all input signal is accounted for. The result of this step plotted using MATLAB plot command and displayed in Figure 3. Figure 3 Frame blocking of the speech signal The next step in the processing is to window each individual frame so as to minimize the signal discontinuities at the beginning and end of each frame. The concept here is to minimize the spectral distortion by using the window to taper the signal to zero at the beginning and end of each frame. If we define the window as w(n), 0 ≤ n ≤ N −1, where N is the number of samples in each frame, then the result of windowing is the signal y (n) = x (n)w(n), 0 ≤ n ≤ N −1 l l