Target speech extractionwith learned spectral bases

In this paper we present a method for extracting a speech signal of target speaker from noisy convolutive mixtures of target speech and an interference source, when training utterances of the target speaker are available. We incorporate a statistical latent variable model into blind source separation (BSS), where we make use of spectral bases learned from the training utterances of the target speaker to identify which source corresponds to the target speaker. Combined with any existing BSS methods, our post-processing (which is the main contribution) consists of two steps: (1) channel selection where we identify the source corresponding to the target speaker; (2) enhancement where we further suppress the remaining interference. Numerical experiments confirm that our method substantially improves the separation quality of existing BSS methods and successfully restores the target speaker´s speech.