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