Third DIHARD Challenge Evaluation

Item Name: Third DIHARD Challenge Evaluation
Author(s): Neville Ryant, Mark Liberman, James Fiumara, Christopher Cieri
LDC Catalog No.: LDC2022S14
ISLRN: 805-666-543-566-5
Release Date: December 15, 2022
Member Year(s): 2022
DCMI Type(s): Sound, Text
Sample Type: pcm
Sample Rate: 16000
Data Source(s): broadcast conversation, meeting speech, microphone conversation, microphone speech, telephone conversations, telephone speech, web collection
Project(s): MIXER, ROAR, VAST
Application(s): diarization, speech activity detection
Language(s): Mandarin Chinese, English
Language ID(s): cmn, eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2022S14 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Ryant, Neville, et al. Third DIHARD Challenge Evaluation LDC2022S14. Web Download. Philadelphia: Linguistic Data Consortium, 2022.
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Third DIHARD Challenge Evaluation was developed by the Linguistic Data Consortium (LDC) and contains approximately 33 hours of English and Chinese speech data along with corresponding annotations used in support of the Third DIHARD Challenge.

The DIHARD Challenges were a set of shared tasks on diarization focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As with the first and second challenges, the third development and evaluation sets were drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, and amateur web videos.


Data sources in this release are as follows (all sources are in English unless otherwise indicated):

All audio is provided in the form of 16 kHz, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Scoring regions for each recording are specific by un-partitioned evaluation map (UEM) files. All annotation file types are encoded as UTF-8. More information about file formats, data sources and domains is contained in the corpus documentation.


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