DiPCo - Dinner Party Corpus

By Maarten Van Segbroeck, A. Zaid, Ksenia Kutsenko, Cirenia Huerta, Tinh Nguyen, X. Luo, Björn Hoffmeister, Jan "Yenda" Trmal, Maurizio Omologo, Roland Maas. 2019

72 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Dialogue & Conversational AI, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jinyu Li
2. Zhuo Chen
3. Takuya Yoshioka
4. Zhong Meng
5. Dong Yu
6. Yi Luo
7. Shinji Watanabe
8. Jian Wu

In Evaluation & Benchmarks

1. Jinyu Li
2. Zhong Meng
3. Zhuo Chen
4. Maja Pantic
5. Liang Lu
6. Yi Luo
7. Emmanuel Vincent
8. Jian Wu

In Dialogue & Conversational AI

1. Emmanuel Vincent

In Signal Processing

1. Jinyu Li
2. Zhuo Chen
3. Takuya Yoshioka
4. Yi Luo
5. Zhong Meng
6. Dong Yu
7. Shinji Watanabe
8. John R. Hershey

Citations in the corpus

• CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings (2020, neutral): “The DIPCO corpus [33], inspired by the CHiME5 challenge [34] but of shorter duration, provides recordings of dinner table interactions between four participants recorded simultaneously on several commercially available microphone arrays.”
• LibriMix: An Open-Source Dataset for Generalizable Speech Separation (2020, background): “Real diner-party recordings [13,18] as well as meeting recordings [19,20] are also available.”
• EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments (2021, methodology): “The Amazon Dinner Party Corpus (DiPCo) [7], released in 2019, contains 39 channels of audio as well as annotations to specifically help address the cocktail party problem in the form of a dinner party.”
• Continuous Speech Separation: Dataset and Analysis (2020, background): “In [32, 33], “dinner party” style data were collected.”