4. Linear prediction cepstral coefficients (LPCC)

Linear prediction cepstral coefficients (LPCC) are cepstral coefficients derived from LPC calculated spectral envelope [11]. LPCC are the coefficients of the Fourier transform illustration of the logarithmic magnitude spectrum [30, 31] of LPC. Cepstral analysis is commonly applied in the field of speech processing because of its ability to perfectly symbolize speech waveforms and characteristics with a limited size of features [31].

It was observed by Rosenberg and Sambur that adjacent predictor coefficients are highly correlated and therefore, representations with less correlated features would be more efficient, LPCC is a typical example of such. The relationship between LPC and LPCC was originally derived by Atal in 1974. In theory, it is relatively easy to convert LPC to LPCC, in the case of minimum phase signals [32].

#### 4.1. Algorithm description, strength and weaknesses

In speech processing, LPCC analogous to LPC, are computed from sample points of a speech waveform, the horizontal axis is the time axis, while the vertical axis is the amplitude axis [31]. Some Commonly Used Speech Feature Extraction Algorithms http://dx.doi.org/10.5772/intechopen.80419 9

Figure 3. Block diagram of LPCC processor.

am <sup>¼</sup> log <sup>1</sup> � km

Linear predictive analysis efficiently selects the vocal tract information from a given speech [16]. It is known for the speed of computation and accuracy [18]. LPC excellently represents the source behaviors that are steady and consistent [23]. Furthermore, it is also be used in speaker recognition system where the main purpose is to extract the vocal tract properties [25]. It gives very accurate estimates of speech parameters and is comparatively efficient for computation [14, 26]. Traditional linear prediction suffers from aliased autocorrelation coefficients [29]. LPC estimates have high sensitivity to quantization noise [30] and might not be well suited for

Linear prediction cepstral coefficients (LPCC) are cepstral coefficients derived from LPC calculated spectral envelope [11]. LPCC are the coefficients of the Fourier transform illustration of the logarithmic magnitude spectrum [30, 31] of LPC. Cepstral analysis is commonly applied in the field of speech processing because of its ability to perfectly symbolize speech waveforms

It was observed by Rosenberg and Sambur that adjacent predictor coefficients are highly correlated and therefore, representations with less correlated features would be more efficient, LPCC is a typical example of such. The relationship between LPC and LPCC was originally derived by Atal in 1974. In theory, it is relatively easy to convert LPC to LPCC, in the case of

In speech processing, LPCC analogous to LPC, are computed from sample points of a speech waveform, the horizontal axis is the time axis, while the vertical axis is the amplitude axis [31].

where am is the linear prediction coefficient, km is the reflection coefficient.

4. Linear prediction cepstral coefficients (LPCC)

and characteristics with a limited size of features [31].

4.1. Algorithm description, strength and weaknesses

generalization [23].

Figure 2. Block diagram of LPC processor.

8 From Natural to Artificial Intelligence - Algorithms and Applications

minimum phase signals [32].

1 þ km 

(5)

The LPCC processor is as seen in Figure 3. It pictorially explains the process of obtaining LPCC. LPCC can be calculated using [7, 15, 33]:

$$\mathcal{C}\_m = a\_m + \sum\_{k=1}^{m-1} \left[ \frac{k}{m} \right] c\_k a\_{m-k} \tag{6}$$

where am is the linear prediction coefficient, Cm is the cepstral coefficient.

LPCC have low vulnerability to noise [30]. LPCC features yield lower error rate as compared to LPC features [31]. Cepstral coefficients of higher order are mathematically limited, resulting in an extremely extensive array of variances when moving from the cepstral coefficients of lower order to cepstral coefficients of higher order [34]. Similarly, LPCC estimates are notorious for having great sensitivity to quantization noise [35]. Cepstral analysis on high-pitch speech signal gives small source-filter separability in the quefrency domain [29]. Cepstral coefficients of lower order are sensitive to the spectral slope, while the cepstral coefficients of higher order are sensitive to noise [15].
