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Speech signals are complex intermingling of various informative factors, andthis information blending makes decoding any of the individual factorsextremely difficult. A natural idea is to factorize each speech frame intoindependent factors, though it turns out to be even more difficult thandecoding each individual factor. A major encumbrance is that the speaker trait,a major factor in speech signals, has been suspected to be a long-termdistributional pattern and so not identifiable at the frame level. In thispaper, we demonstrated that the speaker factor is also a short-time spectralpattern and can be largely identified with just a few frames using a simpledeep neural network (DNN). This discovery motivated a cascade deepfactorization (CDF) framework that infers speech factors in a sequential way,and factors previously inferred are used as conditional variables wheninferring other factors. Our experiment on an automatic emotion recognition(AER) task demonstrated that this approach can effectively factorize speechsignals, and using these factors, the original speech spectrum can be recoveredwith high accuracy. This factorization and reconstruction approach provides anovel tool for many speech processing tasks.

Deep Factorization for Speech Signal