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Target speech extraction, which extracts the speech of a target speaker in amixture given auxiliary speaker clues, has recently received increasedinterest. Various clues have been investigated such as pre-recorded enrollmentutterances, direction information, or video of the target speaker. In thispaper, we explore the use of speaker activity information as an auxiliary cluefor single-channel neural network-based speech extraction. We propose a speakeractivity driven speech extraction neural network (ADEnet) and show that it canachieve performance levels competitive with enrollment-based approaches,without the need for pre-recordings. We further demonstrate the potential ofthe proposed approach for processing meeting-like recordings, where the speakeractivity is obtained from a diarization system. We show that this simple yetpractical approach can successfully extract speakers after diarization, whichresults in improved ASR performance, especially in high overlapping conditions,with a relative word error rate reduction of up to 25%.

Speaker activity driven neural speech extraction