International Journal of Electronics Communication and Computer Engineering^TM

Feature extraction is process of obtaining different features such as power, pitch, and vocal tract configuration from the speech signal. Feature extraction of speech is one of the most important problems in the field of speech recognition and representative of the speech. This phase is proceeded right after the input speech is preemphasized and windowed. A novel method for speech recognition is presented, utilizing nonlinear signal processing techniques to extract time-domain based, reconstructed phase space derived features. These nonlinear methodologies differ strongly from the traditional linear signal processing techniques typically employed for speech recognition. In this paper briefly discuss the signal modeling approach for speech recognition. It is followed by overview of basic operations involved in signal modeling to discover the discriminatory strength of these reconstructed phase space derived features, isolated phoneme are executed and are compared to a baseline classifier that uses Mel frequency cepstral coefficient features. Statistical methods are implemented to model these features. The results demonstrate that reconstructed phase space derived features contain substantial discriminatory power, even though the Mel frequency cepstral coefficient features outperformed them on direct comparisons. When the two feature sets are combined, improvement is made over the baseline, suggesting that the features extracted using the nonlinear techniques contain different discriminatory information than the features extracted from linear approaches. These nonlinear methods are particularly interesting, because they attack the speech recognition problem in a radically different manner and are an attractive research opportunity for improved speech recognition accuracy. Further commonly used spectral and temporal an

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