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

arXiv: https://arxiv.org/abs/2209.11494 · HTML: https://mlsanity.com/paper/2209.11494/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition](https://mlsanity.com/paper/2408.09215/) (2024)
- [A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures](https://mlsanity.com/paper/2306.00634/) (2023)
- [Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription](https://mlsanity.com/paper/2309.08454/) (2023)
- [Long-term Conversation Analysis: Exploring Utility and Privacy](https://mlsanity.com/paper/2306.16071/) (2023)
- [LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices](https://mlsanity.com/paper/2308.10682/) (2023)

## Abstract (arXiv)

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.