Separation of an overlapped signal using speech production models

We propose a novel approach to separating an overlapped signal using speech production models. The speech production models are used as top-down constraints for solving a one-channel signal separation, which is a one-to-many mapping problem. The goal of our approach is to not only realize high-accuracy separation systems but to also construct a computational model of human auditory mechanisms based on perception-production interaction. The utility of the proposed method is demonstrated in a task of separating a mixture of two-speaker vowel utterances using primitive linear speech production models