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Author Markaki, Maria ♦ Stylianou, Yannis
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Discriminative Power ♦ Fea-ture Selection ♦ Mutual Information ♦ Time-varying Information ♦ Classification Task ♦ Initial Dimension ♦ Order Svd ♦ Modulation Frequency Feature ♦ Dimensionality Reduction ♦ Compact Feature ♦ Modulation Spectrum ♦ Subspace Result ♦ Modulation Spectrogram ♦ Speech Discrimination ♦ Principal Ax ♦ Pitch Value ♦ Dimensionality Reduction Method ♦ Index Term ♦ Mod-ulation Frequency Subspace ♦ Phoneme Rate ♦ Modulation Spectral Feature ♦ Target Class ♦ Multilinear Algebra
Description 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
in Proc. Interspeech 2008
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2008-01-01