Mel-Spectrographic Mask Estimation for Missing Data Speech Recognition using Short-Time-Fourier-Transform Ratio Estimators

This paper adopts the framework of DUET, a recently proposed blind source separation (BSS) method, for speech recognition. Based on the attenuation and delay estimation in stereo signals spectrographic masks are designed to extract a target speaker from a mixture containing multiple speech sources. Instead of using these masks for resynthesis we avoid source reconstruction and propose to combine the source separation with a missing data speech recognizer. The obtained results for connected digit experiments in a multi-speaker environment demonstrate the validity of the approach.