Trying to find 33 hand-located 'audio scene boundaries' in the first hour of Blade Runner. Complicated 'listener model' is intended to detect changes in the dominant sound sources. Base features are cepstra, cochlea model outputs, cepstral flux. They then use the net ouptut posteriors as if they were the likelihoods from individual mixture components of a Gaussian mixture codebook, and use EM to estimate weights for these 'mixture components' for each state of various HMM topologies. Interesting comparison to the Tandem Aurora results - maybe we're getting our gain simply from the different HMM topology, not from the GM modeling.
Trick is in reliably recovering syllable breaks and intended polarity.
Could be useful for analyzing the meeting recorder data. I think superimposes several adjacent pitch cycles of the acoustics, based on glottal closure alignment, to help reinforce the speech and cancel the noise. I think they showed results of combining both. Shows that independence measures can be used to prune the inverse matrices, and hence speed up recognition, with little effect on recognition error. Even though identity of classes is unknown, boundaries between different segments are successfully identified.