To Separate Speech

The PASCAL Speech Separation Challenge (SSC) is based on a corpus of sentences from the Wall Street Journal task read by two speakers simultaneously and captured with two circular eight-channel microphone arrays. This work describes our system for the recognition of such simultaneous speech. Our system has four principal components: A person tracker returns the locations of both active speakers, as well as segmentation information for each utterance, which are often of unequal length; two beamformers in generalized sidelobe canceller (GSC) configuration separate the simultaneous speech by setting their active weight vectors according to a minimum mutual information (MMI) criterion; a postfilter and binary mask operating on the outputs of the beamformers further enhance the separated speech; and finally an automatic speech recognition (ASR) engine based on a weighted finite-state transducer (WFST) returns the most likely word hypotheses for the separated streams. In addition to opti...

• Type: Conference
• Year: 2007
• Where: MLMI
• Authors: John W. McDonough, Ken'ichi Kumatani, Tobias Gehrig, Emilian Stoimenov, Uwe Mayer, Stefan Schacht, Matthias Wölfel, Dietrich Klakow
• Keywords: Automatic Speech Recognition, Machine Learning, MLMI 2007, PASCAL Speech Separation, Simultaneous Speech