Feature Extraction of the Speech Signal

  • E. S. Gopi
Part of the Signals and Communication Technology book series (SCT)


Isolated speech recognition, speaker recognition, and continuous speech recognition require the feature vector extracted from the speech signal. This is subjected to pattern recognition to formulate the classifier. The feature vector is extracted from each frame of the speech signal under test. In this chapter, various parameter extraction techniques such as linear predictive co-efficients as the filter co-efficients of the vocal tract model, poles of the vocal tract filter, cepstrual co-efficients, mel-frequency cepstral co-efficients (MFCC), line spectral co-efficients, and reflection co-efficients are discussed in this chapter. The preprocessing techniques such as dynamic time warping, endpoint detection, and pre-emphasis are also discussed in this chapter.


Speech Signal Vocal Tract Dynamic Time Warping Speech Segment Gibbs Phenomenon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Electronics and Communication EngineeringNational Institute of TechnologyTrichyIndia

Personalised recommendations