SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition
Abstract
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.
Cite
@article{arxiv.1910.13934,
title = {SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition},
author = {Lukas Drude and Jens Heitkaemper and Christoph Boeddeker and Reinhold Haeb-Umbach},
journal= {arXiv preprint arXiv:1910.13934},
year = {2019}
}
Comments
Submitted to ICASSP 2020