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

TL;DR

LibriWASN is a new dataset designed for testing meeting separation, diarization, and recognition using asynchronous, multi-device recordings that mimic real-world wireless sensor networks.

Contribution

It introduces a challenging, realistic dataset with asynchronous recordings from multiple devices for advancing meeting analysis systems.

Findings

Dataset includes 29 channels from 9 devices with unsynchronized clocks.

Contains ground-truth diarization for speaker timing.

Designed for testing clock synchronization and separation algorithms.

Abstract

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.