metadata

annotations_creators:
  - expert-generated
license: cdla-permissive-1.0
language_creators:
  - expert-generated
size_categories:
  - 100M<n<100G
language:
  - en
task_categories:
  - automatic-speech-recognition
  - voice-activity-detection
multilinguality:
  - monolingual
task_ids: []
pretty_name: DipCo
tags:
  - speaker separation
  - speech-recognition
  - microphone array processing

DipCo - Dinner Party Corpus, Interspeech 2020

Please consider to use Zenodo Data Backup Link to Download Audio: https://zenodo.org/record/8122551
- Paper: https://www.isca-speech.org/archive/interspeech_2020/segbroeck20_interspeech.html
Author(s):
- Van Segbroeck, Maarten; Zaid, Ahmed; Kutsenko, Ksenia; Huerta, Cirenia; Nguyen, Tinh; Luo, Xuewen; Hoffmeister, Björn; Trmal, Jan; Omologo, Maurizio; Maas, Roland
Contact person(s):
- Maas, Roland; Hoffmeister, Björn
Distributor(s):
- Yang, Huck

Only Download Dipco from Zenodo EU Open Link

wget --limit-rate=5m https://zenodo.org/record/8122551/files/DipCo.tgz?download=1
-czvf DipCo.tgz Dipco/

The ‘DipCo’ data corpus is a new data set that was publicly released by Amazon to help speech scientists address the difficult problem of separating speech signals in reverberant rooms with multiple speakers.

The corpus was created with the assistance of Amazon volunteers, who simulated the dinner-party scenario in the lab. We conducted multiple sessions, each involving four participants. At the beginning of each session, participants served themselves food from a buffet table. Most of the session took place at a dining table, and at fixed points in several sessions, we piped music into the room, to reproduce a noise source that will be common in real-world environments.

Each participant was outfitted with a headset microphone, which captured a clear, speaker-specific signal. Also dispersed around the room were five devices with seven microphones each, which fed audio signals directly to an administrator’s laptop. In each session, music playback started at a given time mark. The close-talk recordings were segmented and separately transcribed.

Sessions

Each session contains the close talk recordings of 4 participants and the far-field recordings from the 5 devices. The following name conventions are used:

sessions have a <session_id> label denoted by ```S01, S02, S03, ...``
participants have a <speaker_id> label denoted by P01, P02, P03, P04, ...
devices have a <device_id> label denoted by U01, U02, U03, U04, U05
array microphone have a <channel_id> label denoted by CH1, CH2, CH3, CH4, CH5, CH6, CH7

We currently have the following sessions:

Session	Participants	Hours [hh:mm]	#Utts	Music start [hh:mm:ss]
S01	P01, P02, P03, P04	00:47	903	00:38:52
S02	P05, P06, P07, P08	00:30	448	00:19:30
S03	P09, P10, P11, P12	00:46	1128	00:33:45
S04	P13, P14, P15, P16	00:45	1294	00:23:25
S05	P17, P18, P19, P20	00:45	1012	00:31:15
S06	P21, P22, P23, P24	00:20	604	00:06:17
S07	P21, P22, P23, P24	00:26	632	00:10:05
S08	P25, P26, P27, P28	00:15	352	00:01:02
S09	P29, P30, P31, P32	00:22	505	00:12:18
S10	P29, P30, P31, P32	00:20	432	00:07:10
The sessions have been split into a development and evaluation set as follows:

Dataset	Sessions	Hours [hh:mm]	#Utts
Dev	S02, S04, S05, S09, S10	02:43	3691
Eval	S01, S03, S06, S07, S08	02:36	3619

The DiPCo data set has the following directory structure:

DiPCo/
├── audio	
│    ├── dev		
│    └── eval	
└── transcriptions	
      ├── dev		
      └── eval

Audio

The audio data is converted into WAV format with a sample rate of 16kHz and 16-bit precision. The close-talk recordings were made by monaural microphone and contain a single channel. The far-field recordings of all 5 devices were microphone array recordings and contain 7 raw audio channels.

The WAV file name convention is as follows:

close talk recording of session <session_id> and participant <speaker_id>
- <session_id>_<speaker_id>.wav, e.g. S01_P03.wav
farfield recording of microphone <channel_id> of session <session_id> and device <device_id>
- <session_id>_<device_id>.<channel_id>.wav, e.g. S02_U3.CH1.wav

Transcriptions

Per session, a JSON format transcription file <session_id>.json has been provided. The JSON files contains for each transcribed utterance the following metadata:

Session ID ("session_id")
Speaker ID ("speaker_id")
- Gender ("gender_id")
- Mother Tongue ("mother_tongue")
- Nativeness ("nativeness")
Transcription ("words")
Start time of utterance ("start_time")
- The close-talk microphone recording of the speaker (close-talk)
- The farfield microphone array recordings of devices with <device_id> label
End time ("end_time")
Reference signal that was used transcribing the audio ("ref")

The following is an example annotation of one utterance in a JSON file:

    {
      "start_time": {
            "U01": "00:02:12.79",
            "U02": "00:02:12.79",
            "U03": "00:02:12.79",
            "U04": "00:02:12.79",
            "U05": "00:02:12.79",
            "close-talk": "00:02:12.79"
        },
          "end_time": {
            "U01": "00:02:14.84",
            "U02": "00:02:14.84",
            "U03": "00:02:14.84",
            "U04": "00:02:14.84",
            "U05": "00:02:14.84",
            "close-talk": "00:02:14.84"
        },
        "gender": "male",
        "mother_tongue": "U.S. English",
        "nativeness": "native",
        "ref": "close-talk",
        "session_id": "S02",
        "speaker_id": "P05",
        "words": "[noise] how do you like the food"
    },

Transcriptions include the following tags:

[noise] noise made by the speaker (coughing, lip smacking, clearing throat, breathing, etc.)
[unintelligible] speech was not well understood by transcriber
[laugh] participant laughing

License Summary

The DiPCo data set has been released under the CDLA-Permissive license. See the LICENSE file.