# LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices

arXiv: https://arxiv.org/abs/2308.10682 · HTML: https://mlsanity.com/paper/2308.10682/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [Continuous Speech Separation with Ad Hoc Microphone Arrays](https://mlsanity.com/paper/2103.02378/) (2021)
- [Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays](https://mlsanity.com/paper/2103.15118/) (2021)
- [MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator](https://mlsanity.com/paper/2209.11494/) (2022)
- [Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones](https://mlsanity.com/paper/2007.15868/) (2020)
- [On Synchronization of Wireless Acoustic Sensor Networks in the Presence of Time-varying Sampling Rate Offsets and Speaker Changes](https://mlsanity.com/paper/2110.12820/) (2021)
- [An Initialization Scheme for Meeting Separation with Spatial Mixture Models](https://mlsanity.com/paper/2204.01338/) (2022)

## Abstract (arXiv)

We present LibriWASN, a data set whose design follows closely the LibriCSS meeting recognition data set, with the marked difference that the data is recorded with devices that are randomly positioned on a meeting table and whose sampling clocks are not synchronized. Nine different devices, five smartphones with a single recording channel and four microphone arrays, are used to record a total of 29 channels. Other than that, the data set follows closely the LibriCSS design: the same LibriSpeech sentences are played back from eight loudspeakers arranged around a meeting table and the data is organized in subsets with different percentages of speech overlap. LibriWASN is meant as a test set for clock synchronization algorithms, meeting separation, diarization and transcription systems on ad-hoc wireless acoustic sensor networks. Due to its similarity to LibriCSS, meeting transcription systems developed for the former can readily be tested on LibriWASN. The data set is recorded in two different rooms and is complemented with ground-truth diarization information of who speaks when.