16. A method, comprising: for each source of a plurality of sources, generating a plurality of word level models, each word level model corresponding to a respective one word of a plurality of words, each word level model including: a plurality of dictionaries, each of the plurality of dictionaries including one or more spectral components, and probabilities of transition between the dictionaries; for each source, combining the word level models into a single source specific model; and constraining the single source specific models according to high level information that defines valid transitions, the constrained single source specific models being usable to perform source separation on a sound mixture that includes multiple sources.
16. A method, comprising: for each source of a plurality of sources, generating a plurality of word level models, each word level model corresponding to a respective one word of a plurality of words, each word level model including: a plurality of dictionaries, each of the plurality of dictionaries including one or more spectral components, and probabilities of transition between the dictionaries; for each source, combining the word level models into a single source specific model; and constraining the single source specific models according to high level information that defines valid transitions, the constrained single source specific models being usable to perform source separation on a sound mixture that includes multiple sources. 17. The method of claim 16 , wherein the high level information is a language model that defines a corpus of words and a plurality of valid sequences of the words of the corpus.