Feature Extraction in Speech Coding and Recognition

speech coding, speech recognition, feature normalization, feature extraction, PLP, MFCC

This document is dealing with the feature extraction techniques generally used in speech recognition tasks. The most popular features used in speech recognition, such as Mel-Filter bank cepstral coefficients (MFCCs) and Perceptual Linear Prediction (PLP) coefficients, were taken as the baseline features. A significant amount of effort has been devoted to establishing speech feature extraction schemes which enable robust and high performance speech recognition in a range of operating environments. Each scheme consists of several processing stages that will be subjected to the theoretical analysis as well as their contribution to the resulting performance of the whole system. In many descriptions, feature extraction is considered as comprising three different stages: - namely static feature extraction -feature normalization -inclusion of temporal information. In our work we have concentrated on first two stages. In all our speech recognition experiments delta and acceleration components were applied as the temporal derivatives.