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

TL;DR

MMS-MSG is a flexible, modular signal generator that creates diverse multi-speaker speech mixtures from any corpus, aiding development, evaluation, and training of speech enhancement systems in various environments.

Contribution

It introduces a versatile, extendable tool for generating realistic multi-speaker mixtures across different acoustic scenarios from any speech corpus.

Findings

Generated datasets enable effective training and evaluation.

Baseline results demonstrate the utility of MMS-MSG in real-world scenarios.

Flexible simulation of diverse acoustic environments.

Abstract

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.