Experiments with linear and nonlinear feature transformations in HMM based phone recognition

Feature extraction is the key element when aiming at robust speech recognition. In this work both linear and nonlinear data-driven feature transformations were applied to the logarithmic mel-spectral context feature vectors in the TIMIT phone recognition task. Transformations were based on Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and multilayer perceptron network based Nonlinear Discriminant Analysis (NLDA). All four methods outperformed the baseline system which consisted of the standard feature representation based on MFCCs with the first-order deltas, using a mixture-of-Gaussians HMM recognizer. Further improvement was gained by forming the feature vector as a concatenation of the outputs of all four feature transformations.