Single-channel speech separation using sparse non-negative matrix factorization

Abstract

We apply machine learning techniques to the problem of separating multiple speech sources from a single microphone recording.The method of choice is a sparse non-negative matrix factorization algorithm, which in an unsupervised manner can learn sparse representations of the data.This is applied to the learning of personalized dictionaries from a speech corpus, which in turn are used to separate the audio stream into its components.We show that computational savings can be achieved by segmenting the training data on a phoneme level.To split the data, a conventional speech recognizer is used.The performance of the unsupervised and supervised adaptation schemes result in significant improvements in terms of the target-to-masker ratio.