Understanding basis functions for vowels based on non-negative matrix factorization

With the advent of hands free devices, speech recognition is of utmost importance but miserably fails to be perfect in a cock-tail party environment without speech separation or speech denoising. There are various techniques available for speech separation but the one technique used nowadays is non-negative matrix factorization (NMF). Non-negative matrix factorization decomposes the mixed signal into components namely the basis functions matrix and activation or weight matrix. Speech separation using NMF is successful, if the mixed signal consists of uncorrelated sources. In this paper we revisit the concept of decomposition using NMF for vowel utterances by various speakers and analyse them to identify correlation which may exist among the basis functions. This may be useful for formulating better training bases which eventually will lead to successful speech separation.