Contemporary Methods for Speech Parameterization by Todor Ganchev

By Todor Ganchev

Contemporary tools for Speech Parameterization deals a common view of short-time cepstrum-based speech parameterization and offers a typical floor for additional in-depth reports at the topic. in particular, it deals a finished description, comparative research, and empirical functionality overview of 11 modern speech parameterization equipment, which compute short-time cepstrum-based speech gains.

Among those are 5 discrete wavelet packet remodel (DWPT)-based, six discrete Fourier rework (DFT)-based speech positive factors and a few in their variations that have been used at the speech reputation, speaker acceptance, and different comparable speech processing projects. the most similarities and adjustments of their computation are mentioned and empirical effects from functionality review in universal experimental stipulations are awarded. the popularity accuracy got at the monophone attractiveness, non-stop speech attractiveness and speaker acceptance initiatives is contrasted opposed to the single acquired for the well known and widespread Mel Frequency Cepstral Coefficients (MFCC).

It is proven that lots of those tools bring about speech gains that do provide aggressive functionality on a undeniable speech processing setup when put next to the venerable MFCC. The final doesn't objective the merchandising of sure speech positive aspects yet as an alternative goals to augment the typical realizing concerning the merits and downsides of a few of the speech parameterization thoughts on hand this day and to supply the foundation for choice of a suitable speech parameterization in each one specific case.

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Function for the different implementations: Eq. 12 with dashed line and marker “x,” and Eq. ” Furthermore, when in Eq. ) function is recovered and the resultant MFCC are guaranteed to have zero-mean value, given some balanced speech signal. 7) between center frequency of the filter and critical bandwidth is not used, the general concept of the MFCC paradigm led to a significant advance in the speech parameterization research. A number of researchers elaborated on the original MFCC design, and novel, biologically motivated speech parameterizations emerged.

In the comparative performance evaluations of multiple speech features, presented in Sects. 5–7, we will conform to the frequency range of Slaney (1998), and will use a LFCC filter-bank of 40 filters, referred to as LFCC-FB40. 3 DFT-Based Speech Parameterization Si ¼ log10 N À1 X 23 ! 4) k¼0 where Si is the output of the ith filter, jSðkÞj2 is the power spectrum, and N is the DFT size. 5) Here r is the LFCC index, and R M is the total number of unique LFCC that can be computed. For larger R, the values of the LFCC with index r !

1 shows the equal-width equal-height filter-bank with 47 filters. 1) is first applied and then the log-energy of the filter-bank outputs is computed as: 26 As discussed in Sect. 4, Slaney (1998) covered the frequency range [133, 6855] Hz with a filter-bank of 40 filters. In the comparative performance evaluations of multiple speech features, presented in Sects. 5–7, we will conform to the frequency range of Slaney (1998), and will use a LFCC filter-bank of 40 filters, referred to as LFCC-FB40. 3 DFT-Based Speech Parameterization Si ¼ log10 N À1 X 23 !

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