Dimensionality reduction of modulation frequency features for speech discrimination
Maria Markaki, Yannis Stylianou · 2008
We describe a dimensionality reduction method for modulation spectral features, which keeps the time-varying information of interest to the classification task. Due to the varying degrees of redundancy and discriminative power of the acoustic and mod-ulation frequency subspaces, we first employ a generalization of SVD to tensors (Higher Order SVD) to reduce dimensions. Projection of modulation spectral features on the principal axes with the higher energy in each subspace results in a compact feature set. We further estimate the relevance of these projec-tions to speech discrimination based on mutual information to the target class. Reconstruction of modulation spectrograms from the “best ” 22 features back to the initial dimensions, shows that modulation spectral features close to syllable and phoneme rates as well as pitch values of speakers are preserved. Index Terms: modulation spectrum, multilinear algebra, fea-ture selection, mutual information, speech discrimination