Spectrographic seam patterns for discriminative word spotting

This paper presents a novel method for deriving patterns for classification of speech sounds. In contrast to conventional methods that attempt to capture time-frequency patterns as represented by spectral envelopes or peaks, our method captures patterns of high-energy tracks, or seams, of maximum “whiteness” across frequency in spectrograms. Our hypothesis is that these seams could potentially carry relatively invariant signatures of underlying sounds. We present a method to derive feature vectors from seam patterns for discriminative word spotting. We show experimentally that spectrographic seam patterns are indeed distinctive for different spoken words, and are effective for word spotting.