8. Conclusion

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 MATLAB.
