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The scope of speech enhancement has changed from a monolithic view of single,independent tasks, to a joint processing of complex conversational speechrecordings. Training and evaluation of these single tasks requires syntheticdata with access to intermediate signals that is as close as possible to theevaluation scenario. As such data often is not available, many works insteaduse specialized databases for the training of each system component, e.gWSJ0-mix for source separation. We present a Multi-purpose Multi-SpeakerMixture Signal Generator (MMS-MSG) for generating a variety of speech mixturesignals based on any speech corpus, ranging from classical anechoic mixtures(e.g., WSJ0-mix) over reverberant mixtures (e.g., SMS-WSJ) to meeting-styledata. Its highly modular and flexible structure allows for the simulation ofdiverse environments and dynamic mixing, while simultaneously enabling an easyextension and modification to generate new scenarios and mixture types. Thesemeetings can be used for prototyping, evaluation, or training purposes. Weprovide example evaluation data and baseline results for meetings based on theWSJ corpus. Further, we demonstrate the usefulness for realistic scenarios byusing MMS-MSG to provide training data for the LibriCSS database.

MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator