A study of two dimensional linear discriminants for ASR

We study the information in the joint time-frequency domain using 1515 dimensional-15 spectral energies and temporal span of 1s-block of spectrogram as features. In this feature space, we first derive 20 joint linear discriminants (JLDs) using linear discriminant analysis (LDA). Using principal component analysis (PCA), we conclude that information in this block of the spectrogram can be analyzed independently across the time and frequency domains. Under this assumption, we propose a sequential design of two dimensional discriminants (CLDs), i.e., spectral discriminants followed by temporal discriminants. We show that these CLDs are similar to first few JLDs and the discriminant features derived from the CLDs outperform those obtained from JLDs in the continuous-digit recognition task.

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