Cross-coding networks for speech classification

What kind of internal representations develop with networks that transform speech of one speaker to that of another? This question is addressed in this paper by a novel supervised coding scheme: cross-coding. Instead of performing auto-association, we train networks to map speech of many speakers to speech of a particular speaker, with intermediate bottlenecks. The internal representations developed are then input to another network trained to label the corresponding sounds. Interestingly, the cross-codings seem to have captured speaker invariant properties in the different sounds. Experiments with multispeaker syllable recognition task show that the proposed scheme outperforms the corresponding multilayered net.