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bark fð Þ¼ <sup>26</sup>:<sup>81</sup> <sup>f</sup>

where bark(f) is the frequency (bark) and f is the frequency (Hz).

16 From Natural to Artificial Intelligence - Algorithms and Applications

term representation are addressed in an ad-hoc way [54].

two or more of the discussed algorithms.

8. Conclusion

MATLAB.

frequency.

1960 þ f

The identification achieved by PLP is better than that of LPC [28], because it is an improvement over the conventional LPC because it effectively suppresses the speaker-dependent information [52]. Also, it has enhanced speaker independent recognition performance and is robust to noise, variations in the channel and microphones [53]. PLP reconstructs the autoregressive noise component accurately [54]. PLP based front end is sensitive to any change in the formant

Figure 6 shows the PLP processor, showing all the steps to be taken to obtain the PLP coefficients. PLP has low sensitivity to spectral tilt, consistent with the findings that it is relatively insensitive to phonetic judgments of the spectral tilt. Also, PLP analysis is dependent on the result of the overall spectral balance (formant amplitudes). The formant amplitudes are easily affected by factors such as the recording equipment, communication channel and additive noise [52]. Furthermore, the time-frequency resolution and efficient sampling of the short-

Table 1 shows a comparison between the six feature extraction techniques that have been explicitly described above. Even though the selection of a feature extraction algorithm for use in research is individual dependent, however, this table has been able to characterize these techniques based on the main considerations in the selection of any feature extraction algorithm. The considerations include speed of computation, noise resistance and sensitivity to additional noise. The table also serves as a guide when considering the selection between any

MFCC, LPC, LPCC, LSF, PLP and DWT are some of the feature extraction techniques used for extracting relevant information form speech signals for the purpose speech recognition and identification. These techniques have stood the test of time and have been widely used in speech recognition systems for several purposes. Speech signal is a slow time varying signal, quasi-stationary, when observed over an adequately short period of time between 5 and 100 msec, its behavior is relatively stationary. As a result of this, short time spectral analysis which includes MFCC, LPCC and PLP are commonly used for the extraction of important information from speech signals. Noise is a serious challenge encountered in the process of feature extraction, as well as speaker recognition as a whole. Subsequently, researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. These methods have also been used in the recognition of sounds. The extracted information will be the input to the classifier for identification purposes. The above discussed feature extraction approaches can be implemented using

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Sabur Ajibola Alim1 \* and Nahrul Khair Alang Rashid<sup>2</sup>

