Development and Evaluation data for Multilingual Everyday Recordings - Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge

Version 1.0
Chua, Victoria Yi Han; Garcia Perera, Leibny Paola; Khudanpur, Sanjeev; Khong, Andy W. H.; Dauwels, Justin; Woon, Fei Ting; Styles, Suzy J, 2023, "Development and Evaluation data for Multilingual Everyday Recordings - Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge", https://doi.org/10.21979/N9/ANXS8Z, DR-NTU (Data), V1, UNF:6:QFBERdU0YulYhMohwDaNWg== [fileUNF]
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Description
The inaugural Multilingual Everyday Recordings - Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge focuses on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom. This dataset contains the development set and evaluation sets for two Tasks in the 2023 MERLIon CCS Challenge, a special session at INTERSPEECH 2023 (Theme: 'Inclusive Spoken Language Science and Technology – Breaking Down Barriers').

As videocalls become increasingly ubiquitous, we present a unique first-of-its-kind Zoom videocall dataset. The MERLIon CCS Challenge tackles automatic language identification and language diarization in a subset of audio recordings from the Talk Together Study, where parents narrated an onscreen wordless picturebook to their child. The main objectives of this inaugural challenge are:
• to benchmark the current and novel language identification and language diarization systems in a code-switching scenario including extremely short utterances;
• to test the robustness of such systems under accented speech;
• to challenge the research community to propose novel solutions in terms of adaptation, training, and novel embedding extraction for this particular set of tasks.

The challenge features language identification (Task 1) and language diarization (Task 2). Two tracks, open and closed, are available. The tracks differ by the data used during system training. More information can be found in the MERLIon CCS Challenge Evaluation Plan and the MERLIon CCS Challenge GitHub.

The public release of the Challenge audio data includes minor revisions following the conclusion of the challenge, constituting no more than .0001% of the labeled data.

Due to the nature of the audio and the data release agreement with the participants, all downloads from this repository will require an agreement to the terms of use. To preview the metadata associated with the datasets contained here, you can access the documentation without downloading any files here.

This collection contains two versions of the data, a legacy archive (LEGACY_ARCHIVE) containing all original files in their original file structure and a set of download files (DOWNLOAD_FILES), formatted for efficient download. In the section below, please click Tree view to see the file structure.
Subject Engineering; Social Sciences
Keyword Speech Processing, Engineering Challenge, Codeswitched Speech, Accented Speech, Language Identification, Language Diarization, Child-directed Speech
Related Publication Woon, F. T., Yogarrajah, E. C., Fong, S., Salleh, N. S. M., Sundaray, S., & Styles, S. J. (2021). Creating a corpus of multilingual parent-child speech remotely: Lessons learned in a large-scale onscreen picturebook sharing task. Frontiers in Psychology, 12. doi: 10.3389/fpsyg.2021.734936
Grant Information
National Research Foundation (NRF): NRF2016-SOL002-011
Nanyang Technological University: CRADLE@NTU grant JHU IO 90071537
Nanyang Technological University: NAP Start Up Grant M4081215.100
License/Data Use Agreement

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MERLIon-CCS-Challenge-2023_Development-Set_Evaluated-Regions_v001.tab
LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Labels_v001/Tabular Data - 20.4 KB
Published Aug 11, 2023
3 Downloads
3 Variables, 247 Observations UNF:6:bDmCMlXNpTBHaqDY+57ZdA==
Contains the timestamps of evaluated regions for language diarization in each audio recording in the MERLIon CCS Challenge development set.
Preview "LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Labels_v001/MERLIon-CCS-Challenge-2023_Development-Set_Evaluated-Regions_v001.tab"
MERLIon-CCS-Challenge-2023_Development-Set_v001_File-List.tab
LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/Tabular Data - 10.5 KB
Published Aug 11, 2023
2 Downloads
1 Variables, 151 Observations UNF:6:DBZ0LJYDQuBp+JpHC4e2wQ==
Contains the filenames of all audio recordings in the MERLIon CCS Challenge development set.
Preview "LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/MERLIon-CCS-Challenge-2023_Development-Set_v001_File-List.tab"
MERLIon-CCS-Challenge-2023_Development-Set_v001_METADATA.txt
LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/Plain Text - 45.6 KB
Published Aug 11, 2023
7 Downloads
MD5: 5729c747c57f7719d16ab554173daf1f
Contains metadata of the MERLIon CCS Challenge development set.
Preview "LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/MERLIon-CCS-Challenge-2023_Development-Set_v001_METADATA.txt"
MERLIon-CCS-Challenge-2023_Development-Set_v001_RELEASE-NOTES.txt
LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/Plain Text - 4.1 KB
Published Aug 11, 2023
5 Downloads
MD5: 2a4512e853e7ffdd47d4bb24550c55ca
Contains the release notes and dataset description of the MERLIon CCS Challenge development set.
Preview "LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/MERLIon-CCS-Challenge-2023_Development-Set_v001_RELEASE-NOTES.txt"
MERLIon-CCS-Challenge-2023_Development-Set_v001_Segment-Lengths-Counts.tab
LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/Tabular Data - 13.2 KB
Published Aug 11, 2023
3 Downloads
5 Variables, 151 Observations UNF:6:cor05m8DAi1IsQux4AFq2w==
Contains total lengths of English and Mandarin speech in milliseconds and number of English and Mandarin segments in each audio recording in the MERLIon CCS Challenge development set.
Preview "LEGACY_ARCHIVE/MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001/MERLIon-CCS-Challenge-2023_Development-Set_v001_Segment-Lengths-Counts.tab"
MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001.zip.001
DOWNLOAD_FILES/MERLIon-CCS-Challenge_Development-Set_v001/Unknown - 1000.0 MB
Published Aug 11, 2023
14 Downloads
MD5: 6b49a389e5d0463f19364bd54d7de3d2
The MERLIon CCS Challenge Development Set Audio and Metadata is split into 5 parts. This is part 1 of 5. Download all 5 parts together and extract the data via 7zip.
MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001.zip.002
DOWNLOAD_FILES/MERLIon-CCS-Challenge_Development-Set_v001/Unknown - 1000.0 MB
Published Aug 11, 2023
17 Downloads
MD5: 3205814e04c75d8e0b30228729cc4716
The MERLIon CCS Challenge Development Set Audio and Metadata is split into 5 parts. This is part 2 of 5. Download all 5 parts together and extract the data via 7zip.
MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001.zip.003
DOWNLOAD_FILES/MERLIon-CCS-Challenge_Development-Set_v001/Unknown - 1000.0 MB
Published Aug 11, 2023
8 Downloads
MD5: 0b4198c578646b6818233ee91dc16986
The MERLIon CCS Challenge Development Set Audio and Metadata is split into 5 parts. This is part 3 of 5. Download all 5 parts together and extract the data via 7zip.
MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001.zip.004
DOWNLOAD_FILES/MERLIon-CCS-Challenge_Development-Set_v001/Unknown - 1000.0 MB
Published Aug 11, 2023
10 Downloads
MD5: 38e9fd24ce55ced246cd52211aa1300a
The MERLIon CCS Challenge Development Set Audio and Metadata is split into 5 parts. This is part 4 of 5. Download all 5 parts together and extract the data via 7zip.
MERLIon-CCS-Challenge_Development-Set_Audio-and-Metadata_v001.zip.005
DOWNLOAD_FILES/MERLIon-CCS-Challenge_Development-Set_v001/Unknown - 293.4 MB
Published Aug 11, 2023
7 Downloads
MD5: 9c290ac8af8279949ec13663f2505984
The MERLIon CCS Challenge Development Set Audio and Metadata is split into 5 parts. This is part 5 of 5. Download all 5 parts together and extract the data via 7zip.
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This dataset is made available under the following terms. Please confirm and/or complete the information needed below in order to continue.

Our Community Norms as well as good scientific practices expect that proper credit is given via citation. Please use the data citation shown on the dataset page.

Custom Dataset Terms — the following Custom Dataset Terms have been defined for this dataset.

To access recordings in these collections, users must agree to the following terms:
I agree to respect the audio recordings:
• I will not presented audio in a context which might cause harm or embarrassment to the speaker. This includes association with racial prejudice, criminality, sexual orientation, religious affiliation, other sensitive material, distressing or unpleasant stimuli in another sensory domain (e.g., unpleasant pictures, unpleasant smells).
• I will not identify any individual as ‘bad at’ any aspect of the task.

I agree to cite the corpus:
• In work arising from this corpus, I will cite the original corpus appropriately.
• Any AI/deep learning derivatives that were developed by training on the data in this corpus must acknowledge this corpus.
• If a speaker has contributed a 'username' for their contribution, the 'username' will appear alongside the filename and citation information in any derivatives. Any derivatives incorporating audio from this dataset must indicate 'some rights reserved' or point to the usage rights of the corpus.

I agree to open access:
• I will not charge others to access the materials in the corpus, or bundle the corpus audio into a for-profit product.
• As an extension of 'fair use,' example audio files can be used to describe the nature of the dataset, a phenomenon of interest in the audio file, or illustrate a procedure in work arising from the corpus, even if the resulting work is a for-profit publication or derivative. Any such transfer of rights to a third party is limited to 1% of the total corpus or 6 whole recordings from the total corpus, whichever is larger. Such uses must include citations to the original along with the statement 'some rights reserved'.
Identifiable information has been redacted with a beep of 440Hz. All participants consented to the release of the redacted audio recordings in the dataset.
Some files in the dataset contain timestamp annotations and language labels for segments in each audio recording in the MERLIon CCS Evaluation set for Task 1 and 2 and are locked for use in future challenges. The files are preserved here for archive integrity for future release.
Any usage of the data in this corpus must be accompanied by citation. Any AI/deep learning derivatives that were developed by training on the data in this corpus must acknowledge this corpus. Where Usernames have been given, Usernames must be presented alongside any vocal samples used as illustrations of method or results. For example, named or listed in the credits of a documentary; named in a digital file published as supplementary material in a journal article; or listed in live demonstrations (e.g., Presentation at academic conferences, Public science lectures). Any derivatives incorporating audio from this dataset must contain the same usage terms.
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