  Various information factors are blended in speech signals, which forms the
primary difficulty for most speech information processing tasks. An intuitive
idea is to factorize speech signal into individual information factors (e.g.,
phonetic content and speaker trait), though it turns out to be highly
challenging. This paper presents a speech factorization approach based on a
novel factorial discriminative normalization flow model (factorial DNF).
Experiments conducted on a two-factor case that involves phonetic content and
speaker trait demonstrates that the proposed factorial DNF has powerful
capability to factorize speech signals and outperforms several comparative
models in terms of information representation and manipulation.
