INTERSPEECH 2006 - ICSLP
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.
Bibliographic reference. Schmidt, Mikkel N. / Olsson, Rasmus K. (2006): "Single-channel speech separation using sparse non-negative matrix factorization", In INTERSPEECH-2006, paper 1652-Thu2FoP.10.