The dimensionality and correlation between acoustic observation vectors and between the components within the vectors are investigated in terms of their impact on the performance of HMM (hidden Markov model)-based speech recognition. The dimensionality and correlation are manipulated with principal component analysis and linear discrimination analysis, on either a continuous density or a discrete density HMM system.
Keywords: HMM, Speech Recognition, PCA, LDA, VQ