By Todor Ganchev
This quantity compares conventional and modern speech parameterization innovations utilized in speech and speaker attractiveness initiatives. the writer bargains a complete description of the 10 most generally used frame-based momentary spectrum research strategies.
Read or Download Contemporary Methods for Speech Parameterization (SpringerBriefs in Electrical and Computer Engineering) PDF
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Extra resources for Contemporary Methods for Speech Parameterization (SpringerBriefs in Electrical and Computer Engineering)
In Fig. 7, only the first 24 filters, which cover the frequency range of [0, 4000] Hz, are shown. As illustrated in the figure, in the HFCC scheme, the overlapping among the filters is different from the traditional – and one filter can overlap not only with its closest neighbors but also with more remote neighbors. The most significant difference in the HFCC, when compared to the earlier MFCC schemes, is that the filter bandwidth is decoupled from the filter spacing. 36 Contemporary Methods for Speech Parameterization Fig.
This speech parameterization technique takes advantage of the latest advances in psychoacoustics known at that time and incorporates three engineering approximations of the properties of human hearing, among which are: (i) the critical-band spectral resolution, (ii) the equal-loudness curve, and (iii) the intensity-loudness power law. In the following, we outline the PLP speech parameterizations, following closely the exposition in Hermansky (1990). edu/~markskow/ 40 Contemporary Methods for Speech Parameterization Let us denote with n the discrete-time index, and with xðnÞ, n ¼ 0; 1; :::; N À 1 a discrete-time speech signal with length of N samples that has been sampled with sampling frequency fs .
In brief, let us denote with n the discrete-time index, and with xðnÞ a discrete-time speech signal that has been sampled with sampling frequency fs . Let us consider that the signal xðnÞ has been pre-processed as explained in Sect. 2 and has been segmented in frames with length of N samples. Each speech segment obtained to this end, represented by sðnÞ, n ¼ 0; 1; :::; N À 1, which was pre-emphasized and weighted by the Hamming window, is subject to the DFT, Àj2pnk SðkÞ ¼ sðnÞ Á exp ; N n¼0 N À1 X k ¼ 0; 1; :::; N À 1; where k is the index of the Fourier coefficients, SðkÞ.