Abstract
Feature extraction is very important as it reduces the signal dimension and hence the computation complexity. Features such as Mel-Frequency Cepstral Coefficients (MFCC), Fourier–Bessel Coefficients (FBCC), Linear Prediction Cepstral Coefficients (LPCC), Perceptual LPCC, and Frequency Domain LP (FDP) are used as primary features for searching speech databases. Primary features are used for converting speech to representations such as seams, patches, lattice, posteriorgrams, and Bag of Acoustic Words (BoAW). Methods like Dynamic Time Warping (DTW) and Minimum Edit Distance (MED) are used for matching primary features/posteriorgrams obtained from query and test database. In this chapter, different features, representations, and matching techniques, popular for searching in speech databases, are discussed.
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