# SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition

arXiv: https://arxiv.org/abs/1910.13934 · HTML: https://mlsanity.com/paper/1910.13934/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 13 of 13)

- [Svarah: Evaluating English ASR Systems on Indian Accents](https://mlsanity.com/paper/2305.15760/) (2023)
- [Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation](https://mlsanity.com/paper/2106.07843/) (2021)
- [Recovering from Privacy-Preserving Masking with Large Language Models](https://mlsanity.com/paper/2309.08628/) (2023)
- [ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior](https://mlsanity.com/paper/2505.05657/) (2025)
- [UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures](https://mlsanity.com/paper/2305.20054/) (2023)
- [Mixture Encoder for Joint Speech Separation and Recognition](https://mlsanity.com/paper/2306.12173/) (2023)
- [Improving Generalization of Speech Separation in Real-World Scenarios: Strategies in Simulation, Optimization, and Evaluation](https://mlsanity.com/paper/2408.16126/) (2024)
- [Towards Robust Waveform-Based Acoustic Models](https://mlsanity.com/paper/2110.08634/) (2021)
- [Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription](https://mlsanity.com/paper/2309.08454/) (2023)
- [Error Analysis in a Modular Meeting Transcription System](https://mlsanity.com/paper/2509.10143/) (2025)
- [On the Application of Diffusion Models for Simultaneous Denoising and Dereverberation](https://mlsanity.com/paper/2508.18833/) (2025)
- [Multi-channel multi-speaker transformer for speech recognition](https://mlsanity.com/paper/2601.02688/) (2026)
- [VM-ARRAYDPS: Virtual Microphone Augmented Diffusion Posterior Sampling for Unsupervised Blind Speech Separation](https://mlsanity.com/paper/2610.09334/) (2026)

## Abstract (arXiv)

We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases we consider all WSJ0+1 utterances and take care of strictly separating the speaker sets present in the training, validation and test sets. When spatializing the data we ensure a high degree of randomness w.r.t. room size, array center and rotation, as well as speaker position. Furthermore, this paper offers a critical assessment of recently proposed measures of source separation performance. Alongside the code to generate the database we provide a source separation baseline and a Kaldi recipe with competitive word error rates to provide common ground for evaluation.