DiPCo -- Dinner Party Corpus

Ahmed Zaid; Bj\"orn Hoffmeister; Cirenia Huerta; Jan Trmal; Ksenia Kutsenko; Maarten Van Segbroeck; Maurizio Omologo; Roland Maas; Tinh Nguyen; Xuewen Luo

DiPCo -- Dinner Party Corpus

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1909.13447 v1 pith:7VKZXHGL submitted 2019-09-30 eess.AS cs.CLcs.SD

DiPCo -- Dinner Party Corpus

Maarten Van Segbroeck , Ahmed Zaid , Ksenia Kutsenko , Cirenia Huerta , Tinh Nguyen , Xuewen Luo , Bj\"orn Hoffmeister , Jan Trmal

show 2 more authors

Maurizio Omologo Roland Maas

This is my paper

classification eess.AS cs.CLcs.SD

keywords corpuscreateddatadinnermicrophonepartyrecordingspeech

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction
cs.SD 2026-07 conditional novelty 6.0

Proxy-supervised joint fine-tuning of a BSRNN separator with ASR, speaker-similarity, VAD and DNSMOS losses on a new 71k real-conversation corpus yields the best SIM and timing F1 on REAL-T.