A Comparative Study on Feature Extraction Technique for Isolated Word Speech Recognition

Digital Speech Signal Processing is the process of converting one type of speech signal representation to another type of representation so as to uncover various mathematical or practical properties of the speech signal and do appropriate processing to support in solving both fundamental and deep troubles of interest. Digital Speech Processing chain has two different main model They are Speech Production Model/Generation Model which deals with acoustic waveform and Speech Perception Model/Recognition Model deals with spectral representation for recognition process. Digital Speech Processing used to achieve reliability, flexibility, accuracy, real implementations on low-cost digital speech processing chip, facility to integrate with multimedia and data, encryptability/security of the data and the data representations via suitable techniques. The overall process of production and recognition of speech is to convert the speech signal from the device or human, and to understand the message is speech chain. In other word, the process of converting the speech signals into acoustic Abstract: One of the common and easier techniques of feature extraction is Mel Frequency Cestrum Coefficient (MFCC) which allows the signals to extract the feature vector. It is used by Dynamic Feature Extraction and provide high performance rate when compared to p technique like LPC. But one of the major drawbacks in this technique is robustness. extraction technique is Relative Spectral feature coefficient and in both the log spectral and the distortions as an additive constant. The high the convolution noise introduced in the channel. The low frame spectral changes. Compared to MFCC feature extraction technique, RASTA filtering reduces the impact of the noise in signals and provide