Third DIHARD Challenge Evaluation Plan

06/04/2020 ∙ by Neville Ryant, et al. ∙ 0

This paper introduces the third DIHARD challenge, the third in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain. The challenge comprises two tracks evaluating diarization performance when starting from a reference speech segmentation (track 1) and diarization from raw audio scratch (track 2). We describe the task, metrics, datasets, and evaluation protocol.



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1 Introduction

DIHARD III is the third in a series of diarization challenges focusing on “hard” diarization; that is, speaker diarization for challenging recordings where there is an expectation that the current state-of-the-art will fare poorly. As with other evaluations in this series, DIHARD III is intended to both (1) support speaker diarization research through the creation and distribution of novel data sets and (2) measure and calibrate the performance of systems on these data sets. The results of the challenge will be presented at a standalone workshop collocated with Interspeech 2020111 in Shanghai, China.

The task evaluated in the challenge is speaker diarization; that is, the task of determining “who spoke when” in a multispeaker environment based only on audio recordings. As with DIHARD I and II, development and evaluation sets will be provided by the organizers, but there is no fixed training set with the result that participants are free to train their systems on any proprietary and/or public data. Once again, these development and evaluation sets will be drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, and YouTube videos. However, there are several key differences from DIHARD II:

  • the diarization from multi-channel audio condition that was evaluated as part of DIHARD II will not be evaluated this year; parties interested in this condition should instead consult the results from track 2 of CHiME-6222, which is essentially a rerun of the DIHARD II multichannel condition

  • the child speech domain present in DIHARD I and II has been removed; while this is an extremely interesting domain, issues with the license restrictions on the data and the nature of the task (diarization of child speech is almost a different task from diarization of adult speech) necessitated its removal for this year’s evaluation

  • 12 hours of previously unexposed audio has been added to the clinical domain

  • for the first time, we are including conversational telephone speech – 20 hours of previously unexposed 2 person English language calls selected from the unreleased Fisher English Phase 2 collection

  • for the first time, DIHARD is partnering with NIST through the OpenSAT evaluation series333; all evaluation activities (registration, system submission, scoring, and leaderboard display) will be conducted using NIST maintained web-interfaces

  • instead of an Interspeech special session, results of the challenge will be presented at a post-evaluation workshop, to be collocated with Interspeech 2020 in Shanghai, China in October 2020; for those who are unable to attend the workshop due to COVID-19 impacts, provisions will be made for remote participation

Participation in the evaluation is open to all who are interested and willing to comply with the rules laid out in this evaluation plan. There is no cost to participate and the web interface, data, scoring software, and any baselines are provided free of charge. Participating teams will have the option of attending the post-evaluation workshop444, to be collocated with Interspeech 2020 in Shanghai, China in October 2020. Information about evaluation registration can be found on the DIHARD III website555

For questions not answered in this document or to join the DIHARD mailing list, please contact

2 Planned Schedule (due to COVID-19, dates may be revised)

  • registration opens – June, 2020

  • development set release – July 13th, 2020

  • evaluation set release – August 10th, 2020

  • scoring server opens – August 10th, 2020

  • evaluation period ends – October 5th, 2020 (midnight Anywhere on Earth)

  • abstract submission deadline – October 5th, 2020 (midnight Anywhere on Earth)

  • workshop – October 25th, 2020

  • system descriptions due – November 20th, 2020 (midnight Anywhere on Earth)

While we will strive to meet the above milestones, continued COVID-19 impacts may require one or more milestones to be delayed. Should this happen, an announcement will be made to the mailing list and the schedule adjusted online and in the evaluation plan. We thank you for your understanding in these trying times.

3 Task

3.1 Task definition

The goal of the challenge is to automatically detect and label all speaker segments in each recording session. Small pauses of 200 ms by a speaker are not considered to be segmentation breaks and should be bridged into a single continuous segment. A pause by a speaker is defined as any segment in which that speaker is not producing a vocalization of any kind. By vocalization, we mean speech, including speech errors and infant babbling, but also vocal noise such as breaths, coughs, lipsmacks, sneezes, laughs, humming or any other noise produced by the speaker by means of the vocal apparatus.

3.2 Tracks

Because system performance is strongly influenced by the quality of the speech segmentation used, two different tracks are covered:

  • Track 1 – Diarization from reference SAD. Systems are provided with a reference speech segmentation that is generated by merging speaker turns in the reference diarization.

  • Track 2 – Diarization from scratch. Systems are provided with just the raw audio input for each recording session and are responsible for producing their own speech segmentation.

3.3 Evaluation conditions

For DIHARD III, we define two partitions of the evaluation data:

  • core evaluation set – a “balanced” evaluation set in which the total duration of each domain666See Section 5 and appendix E for more details regarding the domains. is approximately equal

  • full evaluation set – a larger evaluation set that uses all available selections for each domain and which is, thus, unbalanced with some domains having more audio than others; it is a proper superset of the core evaluation set

The core evaluation set strives for balance across domains so that the evaluation metrics are not dominated by any single domain. It mimics the evaluation set composition from DIHARD I and II. The full evaluation set includes additional material from two domains (CLINICAL and CTS), potentially resulting in more stable metrics at the expense of being unbalanced. All system submissions to all tracks will be scored against both sets and the results reported on the leaderboards.

4 Scoring

System output will be scored by comparison to human reference segmentation with performance evaluated by two metrics:

  • diarization error rate (DER)

  • Jaccard error rate (JER)

4.1 Diarization error rate

Diarization error rate (DER), introduced for the NIST Rich Transcription Spring 2003 Evaluation (RT-03S)777, is the total percentage of reference speaker time that is not correctly attributed to a speaker, where “correctly attributed” is defined in terms of an optimal mapping between the reference and system speakers. More concretely, DER is defined as:


  • TOTAL is the total reference speaker time; that is, the sum of the durations of all reference speaker segments

  • FA is the total system speaker time not attributed to a reference speaker

  • MISS is the total reference speaker time not attributed to a system speaker

  • ERROR is the total reference speaker time attributed to the wrong speaker

Contrary to practice in the NIST RT evaluations, NO forgiveness collar will be applied to the reference segments prior to scoring and overlapping speech WILL be evaluated. For more details please consult section 6 of the RT-09 evaluation plan888 and the source to the NIST md-eval scoring tool999Available as part of the Speech Recognition Scoring Toolkit (SCTK): For DIHARD, we will be using version 22 of md-eval..

4.2 Jaccard error rate

In addition to the primary metric we will score systems using Jaccard error rate (JER), first introduced in DIHARD II. The Jaccard error rate is based on the Jaccard index

101010, a similarity measure used to evaluate the output of image segmentation systems. An optimal mapping between reference and system speakers is determined and for each pair the Jaccard index is computed. The Jaccard error rate is then defined as 1 minus the average of these scores. While similar to DER, it weights every speaker’s contribution equally, regardless of how much speech they actually produced.

More concretely, assume we have reference speakers and system speakers. An optimal mapping between speakers is determined using the Hungarian algorithm so that each reference speaker is paired with at most one system speaker and each system speaker with at most one reference speaker. Then, for each reference speaker the speaker-specific Jaccard error rate is computed as:


  • TOTAL is the duration of the union of reference and system speaker segments; if the reference speaker was not paired with a system speaker, it is the duration of all reference speaker segments

  • FA is the total system speaker time not attributed to the reference speaker; if the reference speaker was not paired with a system speaker, it is 0

  • MISS is the total reference speaker time not attributed to the system speaker; if the reference speaker was not paired with a system speaker, it is equal to TOTAL

The Jaccard error rate then is the average of the speaker specific Jaccard error rates:

As with DER NO forgiveness collar will be applied to the reference segments prior to scoring and overlapping speech WILL be evaluated.

JER and DER are highly correlated with JER typically being higher, especially in recordings where one or more speakers is particularly dominant. Where it tends to track DER is in outliers where the diarization is especially bad, resulting in one or more unmapped system speakers whose speech is not then penalized. In these cases, where DER can easily exceed 500%, JER will never exceed 100% and may be far lower if the reference speakers are handled correctly.

4.3 Scoring regions

In most cases the scoring region for each recording will be the entirety of the recording; that is, for a recording of duration 405.37 seconds, the scoring region will be [0, 405.37]. However, for a small subset of the recordings, personal identifying information (PII) has been removed from the recording, either by low-pass filtering or insertion of tones or zeroing out of samples. For these recordings, the scoring regions consist of the entirety of the recording minus these regions. In both cases the scoring regions will be specified by un-partitioned evaluation map (UEM) files, which will be distributed by LDC as part of the development and evaluation releases. Please see Appendix D for details of the UEM file format.

4.4 Scoring tool

All scoring will be performed using version 1.0.1 of dscore, which is maintained as a github repo at:

To score a set of system output RTTMs sys1.rttm, sys2.rttm, … against corresponding reference RTTMs ref1.rttm, ref2.rttm, … using the un-partitioned evaluation map (UEM) all.uem, the command line would be:

  $ python -u all.uem -r ref1.rttm ref2.rttm  -s sys1.rttm sys2.rttm 

The overall and per-file results for DER and JER (and many other metrics) will be printed to STDOUT as a table. For additional details about scoring tool usage, please consult the documentation for the github repo.

5 Data

5.1 Training data

Participants may use any publicly available and/or proprietary data to train their systems, with the exception of the following previously released corpora, from which portions of the evaluation set are drawn:

  • DCIEM Map Task Corpus (LDC96S38)

  • MIXER6 Speech (LDC2013S03)

  • Digital Archive of Southern Speech (LDC2012S03 and LDC2016S05)

  • DIHARD I and II evaluation sets

Portions of MIXER6 have previously been excerpted for use in the NIST SRE10111111 and SRE12121212 evaluation sets, which also may not be used.

All training data should be thoroughly documented in the system description document (see Appendix F) at the end of the challenge. For a list of suggested training corpora, please consult Appendix E.

5.2 Development and evaluation data

The development and evaluation sets consist of selections of 5-10 minute duration samples131313Excepting data drawn from the WEB VIDEO domain, which range from under 1 minute to more than 10 minutes. drawn from 11 domains. For most domains, the same source is used for both the development and evaluation sets, though in some cases the development and evaluation sets use different sources; where the two sets draw from different sources, this is noted. For a detailed explanation of the domains and sources, please consult Appendix A.

5.2.1 Development data

A development set is provided that mirrors the composition of the evaluation set and which may be used for any purpose, including system training. The full composition of this development set, including domains, the sources drawn on for each domain, and total duration for the core set and full set141414The distinction between “core set” and “full set” is identical to the test set, as discussed in Section 3.3. is presented in Table 1.

Domain Source Core set (hours) Full set (hours)
CTS FISHER 2.00 10
MAP TASK DCIEM 2.53 2.53
MEETING RT04 2.45 2.45
WEB VIDEO VAST 1.89 1.89
TOTAL - 23.73 34.90
Table 1: Composition of the core and full development sets. For explanation of domains and sources, consult Appendix A.

For each recording, the following metadata is provided:

  • the domain

  • the source drawn from

  • the language

  • whether or not it was selected for the core development set

5.2.2 Evaluation data

The full composition of the evaluation sets, including domains, the sources drawn on for each domain, and total duration for the core set and full set is presented in Table 2. Note that this set uses different sources than the development set for two domains:

  • the MEETING domain draws from ROAR instead of RT04

  • the SOCIOLINGUISTIC (FIELD) domain draws from DASS instead of SLX

The following metadata will NOT be provided for the recordings during the evaluation period, but will be revealed at the conclusion of the evaluation:

  • the domain

  • the source drawn from

  • the language

  • whether or not it was selected for the core evaluation set

Domain Source Core set (hours) Full set (hours)
CTS FISHER 2.00 10
MAP TASK DCIEM 2.07 2.07
MEETING ROAR 1.87 1.87
WEB VIDEO VAST 2.07 2.07
TOTAL - 22.48 33.65
Table 2: Composition of the full and core evaluation sets. For explanation of domains and sources, consult Appendix A.

5.2.3 Segmentation

Reference diarization was produced by segmenting the recordings into labeled speaker turns according to the following guidelines:

  • split on pauses 200 ms, where a pause by speaker “S” is defined as any segment of time during which “S” is not producing a vocalization of any kind, where vocalization is defined as any noise produced by the speaker by means of the vocal apparatus151515For instance, speech (including yelled and whispered speech), backchannels, filled pauses, singing, speech errors and disfluencies, infant babbling or vocalizations, laughter, coughs, breaths, lipsmacks, and humming.

  • attempt to place boundaries within 10 ms of the true boundary, taking care not to truncate sounds at edges of words (e.g., utterance-final fricatives or utterance initial stops)

  • where close-talking microphones exist for each speaker (e.g., ROAR), perform the segmentation separately for each speaker using their individual microphone

Reference SAD was then derived from these segmentations by merging overlapping speech segments and removing speaker identification.

During DIHARD II, it was found that manual annotation to this spec required use of highly skilled and experienced annotators using multiple spectrogram displays, making the annotation extremely slow and costly. Many annotators were incapable of performing the task even after extensive training and the remainder (usually people with experience in both ASR and acoustic phonetics) found it extremely laborious with real time rates typically greater than 15X and sometimes exceeding 30X161616Recordings from VAST and SEEDLINGS (removed for DIHARD III) were found to be particularly difficult.. Consequently, for DIHARD III we abandoned a commitment to entirely manual segmentation. Where a manual segmentation to these specs already exists (i.e., files annotated for DIHARD II), we use it. For all other data we instead produce a careful turn-level transcription (if one does not exist), then establish boundaries using a forced aligner trained using Kaldi. For DIHARD III, forced alignment was used for the following domains:

  • CTS


For all other domains we reused the manual annotation produced for DIHARD II.

5.2.4 Pii

A limited number of recordings from ADOS, CIR, and DASS contained regions carrying personal identifying information (PII), which had to be removed prior to publication. As systems have no way of plausibly dealing with these regions, they will not be scored and the relevant UEM files reflect this. The method used to de-identify these regions differs from source to source, with some opting to replace PII containing regions with a pure tone, while others used an approach based on low-pass filtering. Please see Appendix A for details about how PII was dealt with for each source.

5.2.5 File formats

All audio and annotations will be distributed via LDC. The audio will be distributed as single channel, 16 bit FLAC files sampled at 16 kHz, while reference speech segmentations will be distributed as HTK label files. In the case of the development set, a reference diarization will be provided, which will be distributed as Rich Transcription Time Marked (RTTM) files. For details regarding these file formats, please see Appendix B and Appendix C.

6 Evaluation rules

There is no cost to participate in the DIHARD evaluation series. Participation is open to all who comply with the evaluation rules set forth in this plan. Development data and evaluation data will be made available to registered participants via LDC. Participants will apply their systems to the evaluation data locally and upload their system outputs to the DIHARD scoring server171717Generously hosted by NIST’s OpenSAT program. for scoring.

All participants agree to process the data in accordance with the following rules:

  • Participants agree to make at least one valid primary system submission to track 1 before the end of the evaluation period. A valid submission is defined as one that contains RTTMs for all recordings and passes the validation step during upload.

  • While most of the test data is actually, or effectively, unexposed, portions have been exposed in part in the following corpora:

    • DCIEM Map Task Corpus (LDC96S38)

    • MIXER6 Speech (LDC2013S03)

    • Digital Archive of Southern Speech (LDC2012S03 and LDC2016S05)

    • NIST SRE10 evaluation data

    • NIST SRE12 evaluation data

    • DIHARD I and II evaluation sets

    Participants agree to not use these corpora for system training or development.

  • Manual/human investigation of the evaluation set (e.g., listening, segmentation, or transcription) prior to the end of the evaluation is disallowed.

  • Participants are allowed to use any automatically derived information (e.g., automatic identification of the domain) for the development and evaluation files provided that the systems used were not trained using any of the prohibited corpora.

  • Participants may make multiple primary and contrastive submissions during the evaluation period (up to 50 valid submissions for each track181818This limit is for primary and contrastive submissions COMBINED. For instance, a team could make 15 primary and 35 contrastive submissions to track 1 or 40 primary and 10 contrastive submissions, but not 15 primary and 40 contrastive submissions.). For each track the leaderboards will display for each team the result of the the most recently processed valid primary system submission.

In addition to the above data processing rules, the participants agree to comply with the following general requirements:

  • Participants agree to submit by the designated deadline (see Section 2) a system description document describing the algorithms, data, and computational resources used for systems. These documents will be submitted at the end of the evaluation and should follow the format set forth in Appendix F.

  • Participants agree to allow the deposit of the RTTM outputs of their final primary system outputs (i.e., those displayed on the leaderboards at the end of the evaluation) on Zenodo. At the conclusion of the challenge, the organizers will deposit an archive on Zenodo containing all system descriptions and final system outputs.

Sites failing to abide by the above rules will be excluded from future evaluation participation and their registrations will not be accepted until they are committed to fully participate.

7 Evaluation protocol

All evaluation activities will be conducted over a NIST maintained web-interface to facilitate information exchange between evaluation participants and the organizers.

7.1 Setting up an evaluation account

Participants must sign up for an evaluation account, which will allow them to perform various activities such as registering for the evaluation, signing the data license agreement, and uploading submissions. To sign up for an evaluation account, follow the instructions at:

After the evaluation account is confirmed, the participant will be asked to join a site (or create one if it does not exist). The participant is also asked to associate their site to a team or to create a team if one does not exist. This allows multiple members to perform activities on behalf of their site and/or team (e.g., make a submission). Clarifying the distinction between participants, sites, and teams:

  • site – a single organization (e.g., NIST)

  • team – a group of organizations collaborating on a task (e.g., Team1 consisting of NIST and LDC)

  • participant – a member or representative of a site who takes part in the evaluation (e.g., John Doe)

7.2 Evaluation registration

One participant from a site must formally register his site to participate in the evaluation by agreeing to the terms of participation; see Section 6.

7.3 Data license agreement

One participant from each site must sign and upload the LDC data license agreement. After the license agreement is confirmed by LDC, LDC will provide instructions for accessing the development and evaluation data.

7.4 Results submission

All system outputs must be submitted via the evaluation dashboard. Additional instructions will be posted on the website when the evaluation server opens.

8 Workshop

The results of the challenge will be presented on October 25th at a workshop collocated with INTERSPEECH 2020191919 in Shanghai. Due to ongoing COVID-19 related disruptions, provisions will be made for remote participation in all phases of the workshop. Teams wishing to submit 2 page extended abstracts to this workshop should follow the instructions at:

9 Updates

Updates to this evaluation plan will be made available via the mailing list and the challenge website (

Appendix A Single Channel Condition Domains and Sources


  • Audiobooks
    Excerpts from recordings of speakers reading aloud passages from public domain English language texts. The recordings were selected from LibriVox and each recording consists of a single, amateur reader. Care was taken to make sure that the chapters and speakers drawn from were not present in LibriSpeech, which also draws from LibriVox.

  • Broadcast interview
    Student-lead radio interviews conducted during the 1970s with popular figures of the era (e.g., Ann Landers, Mark Hamill, Buckminster Fuller, and Isaac Asimov). The recordings are selected from the unpublished LDC YouthPoint corpus.

  • Clinical
    Recordings of Autism Diagnostic Observation Schedule (ADOS) interviews conducted to identify whether a child fit the clinical diagnosis for autism. ADOS is a roughly hour long semi-structured interview in which clinicians attempt to elicit language that differentiates children with Autism Spectrum Disorder from those without (e.g., “What does being a friend mean to you?”). The children included in this collection ranged from 12-16 years in age and exhibit a range of diagnoses from autism to non-autism language disorder to ADHD to typically developing. Interviews are typically recorded for quality assurance purposes; in this case, the recording was conducted using a ceiling mounted microphone. The recordings are selected from the unpublished LDC ADOS corpus.

  • Courtroom
    Recordings of oral arguments from the 2001 term of the U.S. Supreme Court. The original recordings were made using individual table-mounted microphones, one for each participant, which could be switched on and off by the speakers as appropriate. The outputs of these microphones were summed and recorded on a single-channel reel-to-reel analogue tape recorder. All recordings are taken from SCOTUS, an unpublished LDC corpus.

  • CTS
    Conversational telephone speech (CTS) consisting of 10 minute conversations between two native English speakers. All calls are drawn from the unreleased Phase II calls from the Fisher English collection conducted as part of the DARPA EARS project.

  • Map task
    Recordings of pairs of speakers engaged in a map task. Each map task session contains two speakers sitting opposite one another at a table. Each speaker has a map visible only to him and a designated role as either “Leader” or “Follower”. The Leader has a route marked on his map and is tasked with communicating this route to the Follower so that he may precisely reproduce it on his own map. Though each speaker was recorded on a separate channel via a close-talking microphone, these have been mixed together for the DIHARD releases. The recordings are drawn from the DCIEM Map Task Corpus (LDC96S38).

  • Meeting
    Recordings of meetings containing between 3 and 7 speakers. The speech in these meetings is highly interactive in nature consisting of large amounts of spontaneous speech containing frequent interruptions and overlapping speech. For each meeting a single, centrally located distant microphone is provided, which may exhibit excessively low gain. For the development set, these meetings are drawn from RT04, while for the evaluation set they are drawn from ROAR.

  • Restaurant
    Informal conversations recorded in restaurants using binaural microphones. Each session contains between 4 and 7 speakers seated at the same table at a restaurant at lunchtime and was recorded from a binaural microphone worn by a designated facilitator; the mix of the two channels recorded by this microphone are provided. This data exhibits the following properties, which are expected to make it particularly challenging for automated segmentation and recognition:

    • due to the microphone setup, the majority of the speakers are farfield

    • background speech from neighboring tables is often present, sometimes at levels close to that of the primary speakers in the conversation

    • background noise is abundant with clinking silverware, moving chairs/tables, and loud music all common

    • the conversations are informal and highly interactive with interruptions and frequent overlapped speech

    All data is taken from LDC’s unpublished CIR corpus.

  • Sociolinguistic field recordings
    Sociolinguistic interviews recorded under field conditions. Recordings consists of a single interviewer attempting to elicit vernacular speech from an informant during informal conversation. Typically, interviews were recorded in the home, though occasionally they were recorded in a public location such as a park or cafe. The development set recordings were drawn from SLX and the evaluation set from DASS.

  • Sociolinguistic lab recordings
    Sociolinguistic interviews recorded under quiet conditions in a controlled environment. All data is taken from the PZM microphones of LDC’s Mixer 6 collection (LDC23013S03).

  • Web video
    English and Mandarin amateur videos collected from online video sharing sites (e.g., YouTube and Vimeo). This domain is expected to be particularly challenging as the videos present a diverse set of topics and recording conditions; in particular, many videos contain multiple speakers talking in a noisy environment, where it can be difficult to distinguish speech from other kinds of sounds. All data is selected from LDC’s VAST collection.


  • ADOS
    ADOS is an unpublished LDC corpus consisting of transcribed excerpts from ADOS interviews conducted at the Center for Autism Research (CAR) at the Children’s Hospital of Philadelphia (CHOP). All interviews were conducted at CAR by trained clinicians using ADOS module 3. The interviews were recorded using a mixture of cameras and audio recorded from a ceiling mounted microphone. Portions of these interviews determined by a clinician to be particularly diagnostic were then segmented and transcribed.

    Note that in order to publish this data, it had to be de-identified by applying a low-pass filter to regions identified as containing personal identifying information (PII). Pitch information in these regions is still recoverable, but the amplitude levels have been reduced relative to the original signal. Filtering was done with a 10th order Butterworth filter with a passband of 0 to 400 Hz. To avoid abrupt transitions in the resulting waveform, the effect of the filter was gradually faded in and out at the beginning and end of the regions using a ramp of 40 ms.

  • CIR
    Conversations in Restaurants (CIR) is a collection of informal speech recorded in restaurants that LDC originally produced for the NSF Hearables Challenge202020, an NSF-sponsored challenge designed to promote the development of algorithms or methods that could improve hearing in a noisy setting. It consists of conversations between 3 and 6 speakers, all LDC or Penn employees, seated at the same table at a restaurant near the University of Pennsylvania campus. Recording sessions were held at lunch time using a rotating list of restaurants exhibiting diverse acoustic environments and typically lasted 60-70 minutes. All recordings were conducted using binaural microphones mounted on either side of one speaker’s head.

    A limited number of regions from one recording were found to contain PII. These regions were de-identified using the same low-pass filtering approach as in ADOS.

  • DASS
    The Digital Archive of Southern Speech, or DASS, is a corpus of interviews (each lasting anywhere from 3 to 13 hours) recorded during the late 60s and 70s in the Gulf Coast region of the United States. It is part of the larger Linguistic Atlas of the Gulf States (LAGS), a long-running project that attempted to preserve the speech of a region encompassing Louisiana, Alabama, Mississippi, and Florida as well as parts of Texas, Tennessee, Arkansas, and Georgia. Each interview was conducted in the field by a trained interviewer, who attempted to elicit conversation about common topics like family, the weather, household articles, agriculture, and social connections. It is distributed by LDC as LDC2012S03 and LDC2016S05.

    Due to the nature of the interviews, they sometimes contain PII or sensitive materials. All such regions have been replaced by tones of matched duration. Unfortunately, this process does not appear to have been systematic, with the result that the type of tone (pure or complex), power, and frequency differs across the corpus.


    The DCIEM Map Task Corpus (LDC96S38) is a collection of recordings of two-person map tasks recorded for the DCIEM Sleep Deprivation Study. This study was conducted by the Defense and Civil Institute of Environmental Medicine (Department of National Defense, Canada) to evaluate the effect of drugs on performance degradation in sleep deprived individuals. Three drug conditions (Modafinil vs. Amphetamine vs. placebo) were crossed with three sleep conditions (18 hours vs. 48 hours vs. 58 hours awake). During each session, subjects performed a battery of neuropsychological tests (e.g., tracking tasks, time estimation tasks, attention-splitting tasks), questionnaires, and a map task. All audio was recorded via close-talking microphones under quiet conditions.

  • LibriVox
    LibriVox212121 is a collection of public domain audiobooks read by volunteers from around the world. It consists of more than 10,000 recordings in 96 languages. Portions have previously appeared in the popular LibriSpeech222222 corpus, though care was taken to ensure that DIHARD did not select from this subset.

    The Fisher corpora232323 are a series of conversational telephone speech (CTS) collections undertaken as part of the DARPA EARS (Effective, Affordable, Reusable Speech-to-text) program. Each session consisted of a conversation between two randomly assigned (potentially non-native) speakers, who were prompted to talk for up to ten minutes on a randomly assigned topic. A wide range of topic prompts were used, examples of which include:

    • Do either of you have a favorite TV sport? How many hours per week do you spend watching it and other sporting events on TV?

    • Do you think most people would remain calm, or panic during a terrorist attack? How do you think each of you would react?

    • Do you like cold weather or warm weather activities the best? Do you like outside or inside activities better? Each of you should talk about your favorite activities.

    All calls were recorded by LDC and transcribed by a combination of LDC and BBN.

    The Fisher English collection was performed in two phases, the first of which, comprising approximately 2,000 hours of audio from 12,000 speakers, has been released by LDC under catalog entries LDC2004S13, LDC2004T19, LDC2005S13, and LDC2005T19. A further 1,400 hours (from 1,300 speakers) were collected and transcribed during Phase II, but never released. For DIHARD III, we draw from these previously unexposed recordings.

  • MIXER6
    Mixer 6 (LDC2013S03) is a large-scale collection of English speech across multiple environments, modalities, degrees of formality, and channels that was conducted at LDC from 2009 through 2010. The collection consists of interviews with 594 native speakers of English spanning 1,425 sessions, each roughly 40-45 minutes in duration. Each session contained multiple components (e.g., informal conversation styled after a sociolinguistic interview or transcript reading) and was captured by a variety of microphones, including lavalier, head-mounted, podium, shotgun, PZM, and array microphones. While the corpus was released without speaker segmentation or transcripts, a portion of the corpus was subsequently transcribed at LDC. DIHARD II draws its selections from this subset.

  • ROAR
    ROAR is a collection of multiparty (3 to 6 participant) conversations recorded by LDC as part of the DARPA ROAR (Robust Omnipresent Automatic Recognition) project in Fall 2001. While portions of this collection have previously been exposed during the NIST RT evaluations, all DIHARD data comes from previously unexposed meetings. The meetings were recorded at LDC in a purpose built room using a combination of lavalier, head mounted, omnidirectional, PZM, shotgun, podium, and array microphones. For each meeting, a single centrally located distant microphone is provided.

  • RT04
    RT04 consists of meeting speech released as part of the NIST Spring 2004 Rich Transcription (RT-04S) Meeting Recognition Evaluation development and evaluation sets. This data was later re-released by LDC as LDC2007S11 and LDC2007S12. It consists of recordings of multiparty (3 to 7 participant) meetings held at multiple sites (ICSI, NIST, CMU, and LDC), each with a different microphone setup. For DIHARD, a single channel is distributed for each meeting, corresponding to the RT-04S single distant microphone (SDM) condition. Audio files have been trimmed from the original recordings to the 11 minute scoring regions specified in the RT-04S un-partitioned evaluation map (UEM) files242424In cases where the onset or offset of a scoring region was found to bisect a speaker turn, it was adjusted to fall in silence adjacent to the relevant turn..

    SCOTUS is an unpublished LDC corpus consisting of oral arguments from the 2001 term of the U.S. Supreme Court. The recordings were transcribed and manually word-aligned as part of the OYEZ252525 project, then forced aligned and QCed at LDC.

  • SLX
    SLX (LDC2003T15) is a corpus of sociolinguistic interviews conducted in the 1960s and 1970s by Bill Labov and his students. The interview subjects range in age from 15 to 81 and represent a diverse sampling of ethnicities, backgrounds, and dialects (e.g., southern Amercian English, African American English, northern England, and Scotland). While the recordings have good sound quality for field recordings (especially from that era), they were collected in a range of environments ranging from noisy homes (e.g., small children running around in the background) to public parks to gas stations.

  • VAST
    The Video Annotation for Speech Technologies (VAST) corpus is a (mostly) unexposed collection of approximately 2,900 hours of web videos (e.g., YouTube and Vimeo) intended for development and evaluation of speech technologies; in particular, speech activity detection (SAD), diarization, language identification (LID), speaker identification (SID), and speech recognition (STT). Collection emphasized videos where people are talking with a particular emphasis on videos where the speakers spoke primarily English, Mandarin, and Arabic, which comprise the bulk of the corpus262626Eight languages are represented in total: Arabic, English, Mandarin, Min Nan, Spanish, Portuguese, Russian, and Polish.. Portions of this corpus have been exposed previously as part of the NIST 2017 Speech Analytic Technologies Evaluation, the NIST 2017 Language Recognition Evaluation, NIST 2018 Speaker Recognition Evaluation, and DIHARD I and II.

  • YouthPoint
    YouthPoint is an unpublished LDC corpus consisting of episodes of YouthPoint, a late 1970s radio program run by students at the University of Pennsylvania. The show had an interview format similar to shows such as NPR’s Fresh Air and consisted of interviews between University of Pennsylvania students and various popular figures. The recordings were conducted in a studio on open reel tapes and later digitized and transcribed at LDC.

Appendix B Speech segmentation label files

For each recording, the reference speech segmentation will be provided via an HTK label file listing one segment per line, each line consisting of three space-delimited fields:

  • segment onset in seconds from beginning of recording

  • segment offset in seconds from beginning of recording

  • segment label (always “speech”)

For example:

  • 0.10 1.41 speech

  • 1.98 3.44 speech

  • 5.0 7.52 speech

The segments in these files are guaranteed to be disjoint and to not extend beyond the boundaries of the recording session.

Appendix C RTTM File Format Specification

Systems should output their diarizations as Rich Transcription Time Marked (RTTM) files. RTTM files are text files containing one turn per line, each line containing ten space-delimited fields:

  • Type – segment type; should always by “SPEAKER”

  • File ID – file name; basename of the recording minus extension (e.g., “rec1_a”)

  • Channel ID – channel (1-indexed) that turn is on; should always be “1”

  • Turn Onset – onset of turn in seconds from beginning of recording

  • Turn Duration – duration of turn in seconds

  • Orthography Field – should always by “NA

  • Speaker Type – should always be “NA

  • Speaker Name – name of speaker of turn; should be unique within scope of each file

  • Confidence Score – system confidence (probability) that information is correct; should always be “


  • Signal Lookahead Time – should always be “NA

For instance:

  • SPEAKER CMU20020319-1400d01NONE 1 130.430000 2.350 NA NA juliet NA NA

  • SPEAKER CMU20020319-1400d01NONE 1 157.610000 3.060 NA NA tbc NA NA

  • SPEAKER CMU20020319-1400d01NONE 1 130.490000 0.450 NA NA chek NA NA

Appendix D UEM File Format Specification

Un-partitioned evaluation map (UEM) files are used to specify the scoring regions within each recording. For each scoring region, the UEM file contains a line with the following four space-delimited fields

  • File ID – file name; basename of the recording minus extension (e.g., “rec1a”)

  • Channel ID – channel (1-indexed) that scoring region is on

  • Onset – onset of scoring region in seconds from beginning of recording

  • Offset – offset of scoring region in seconds from beginning of recording

For instance:

  • CMU20020319-1400d01NONE 1 125.000000 727.090000

  • CMU20020320-1500d01NONE 1 111.700000 615.330000

  • ICSI20010208-1430d05NONE 1 97.440000 697.290000

Appendix E Data Resources for Training

This appendix identifies a (non-exhaustive) list of publicly available corpora suitable for system training.

Corpora containing meeting speech
LDC corpora

  • ICSI Meeting Speech Speech (LDC2004S02)

  • ICSI Meeting Transcripts (LDC2004T04)

  • ISL Meeting Speech Part 1 (LDC2004S05)

  • ISL Meeting Transcripts Part 1 (LDC2004T10)

  • NIST Meeting Pilot Corpus Speech (LDC2004S09)

  • NIST Meeting Pilot Corpus Transcripts and Metadata (LDC2004T13)

  • 2004 Spring NIST Rich Transcription (RT-04S) Development Data (LDC2007S11)

  • 2004 Spring NIST Rich Transcription (RT-04S) Evaluation Data (LDC2007S12)

  • 2006 NIST Spoken Term Detection Development Set (LDC2011S02)

  • 2006 NIST Spoken Term Detection Evaluation Set (LDC2011S03)

  • 2005 Spring NIST Rich Transcription (RT-05S) Evaluation Set (LDC2011S06)

Non-LDC corpora

Conversational telephone speech (CTS) corpora
LDC corpora

  • CALLHOME Mandarin Chinese Speech (LDC96S34)

  • CALLHOME Spanish Speech (LDC96S35)

  • CALLHOME Japanese Speech (LDC96S37)

  • CALLHOME Mandarin Chinese Transcripts (LDC96T16)

  • CALLHOME Spanish Transcripts (LDC96T17)

  • CALLHOME Japanese Transcripts (LDC96T18)

  • CALLHOME American English Speech (LDC97S42)

  • CALLHOME German Speech (LDC97S43)

  • CALLHOME Egyptian Arabic Speech (LDC97S45)

  • CALLHOME American English Transcripts (LDC97T14)

  • CALLHOME German Transcripts (LDC97T15)

  • CALLHOME Egyptian Arabic Transcripts (LDC97T19)

  • CALLHOME Egyptian Arabic Speech Supplement (LDC2002S37)

  • CALLHOME Egyptian Arabic Transcripts Supplement (LDC2002T38)

  • Switchboard-1 Release 2 (LDC97S62)

  • Fisher English Training Speech Part 1 Speech (LDC2004S13)

  • Fisher English Training Speech Part 1 Transcripts (LDC2004T19)

  • Arabic CTS Levantine Fisher Training Data Set 3, Speech (LDC2005S07)

  • Fisher English Training Part 2, Speech (LDC2005S13)

  • Arabic CTS Levantine Fisher Training Data Set 3, Transcripts (LDC2005T03)

  • Fisher English Training Part 2, Transcripts (LDC2005T19)

  • Fisher Levantine Arabic Conversational Telephone Speech (LDC2007S02)

  • Fisher Levantine Arabic Conversational Telephone Speech, Transcripts (LDC2007T04)

  • Fisher Spanish Speech (LDC2010S01)

  • Fisher Spanish - Transcripts (LDC2010T04)

Other corpora
LDC corpora

  • Speech in Noisy Environments (SPINE) Training Audio (LDC2000S87)

  • Speech in Noisy Environments (SPINE) Evaluation Audio (LDC2000S96)

  • Speech in Noisy Environments (SPINE) Training Transcripts (LDC2000T49)

  • Speech in Noisy Environments (SPINE) Evaluation Transcripts (LDC2000T54)

  • Speech in Noisy Environments (SPINE2) Part 1 Audio (LDC2001S04)

  • Speech in Noisy Environments (SPINE2) Part 2 Audio (LDC2001S06)

  • Speech in Noisy Environments (SPINE2) Part 3 Audio (LDC2001S08)

  • Speech in Noisy Environments (SPINE2) Part 1 Transcripts (LDC2001T05)

  • Speech in Noisy Environments (SPINE2) Part 2 Transcripts (LDC2001T07)

  • Speech in Noisy Environments (SPINE2) Part 3 Transcripts (LDC2001T09)

  • Santa Barbara Corpus of Spoken American English Part I (LDC2000S85)

  • Santa Barbara Corpus of Spoken American English Part II (LDC2003S06)

  • Santa Barbara Corpus of Spoken American English Part III (LDC2004S10)

  • Santa Barbara Corpus of Spoken American English Part IV (LDC2005S25)

  • HAVIC Pilot Transcription (LDC2016V01)

  • Nautilus Speaker Characterization (LDC2018S17)

  • SRI Speech-Based Collaborative Learning Corpus (LDC2019S01)

Appendix F System descriptions

In order to allow proper interpretation of the evaluation results, each submitted system must be accompanied by a system description. System descriptions are expected to be of sufficient detail for a fellow researcher to understand the approach and data/computational resources used to train and run the system. In order to make the preparation and format as consistent as possible, participants should use the IEEE Conference proceedings templates:

with the following document structure:

  • Section 1: Authors

  • Section 2: Abstract

  • Section 3: Notable highlights

  • Section 4: Data resources

  • Section 5: Detailed description of algorithm

  • Section 6: Results on the development set

  • Section 7: Hardware requirements

Section 1: Authors
Listing of people whose contributions you wish acknowledged. This section is optional.

Section 2: Abstract
A short (a few sentences) high-level description of the system.

Section 3: Notable highlights
A brief summary of what is different or any notable highlights. Examples of highlights could be differences among systems submitted; novel or unusual approaches, or approaches/features that led to a significant improvement in system performance.

Section 4: Data resources
This section should describe the data used for training including both volumes and sources. For LDC or ELRA corpora, catalog ids should be supplied. For other publicly available corpora (e.g., AMI) a link should be provided. In cases where a non-publicly available corpus is used, it should be described in sufficient detail to get the gist of its composition. If the system is composed of multiple components and different components are trained using different resources, there should be an accompanying description of which resources were used for which components.

Section 5: Detailed description of algorithm

Each component of the system should be described in sufficient detail that another researcher would be able to reimplement it. You may be brief or omit entirely description of components that are standard (i.e., no need to list the standard equations underlying an LSTM or GRU). If hyperparameter tuning was performed, there should be detailed description both of the tuning process and the final hyperparameters arrived at.

We suggest including subsections for each major phase in the system. Suggested subsections:

  • signal processing – e.g., signal enhancement, denoising, source separation

  • acoustic features – e.g., MFCCs, PLPs, mel fiterbank, PNCCs, RASTA, pitch extraction

  • speech activity detection details – relevant only for tracks 2 and 4

  • segment representation – e.g., i-vectors, d-vectors

  • speaker estimation – how number of speakers was estimated if such estimation was performed

  • clustering method – e.g., k-means, agglomerative

  • resegmentation details

Section 6: Results on the development set
Teams must report performance of the submission systems (both primary and contrastive) on the DIHARD III core and full development sets. Both DER and JER should be reported as output by the the official scoring tool. Optionally, teams may report results of additional experiments (e.g., results from additional systems or on additional datasets). Teams are encouraged to quantify the contribution of their major system components that they believe resulted in significant performance gains, if any.

Section 7: Hardware requirements
System developers should report the hardware requirements for both training and at test time:

  • Total number of CPU cores used

  • Description of CPUs used (model, speed, number of cores)

  • Total number of GPUs used

  • Description of GPUs used (model, single precision TFLOPS, memory)

  • Total number of TPUs used

  • Generations of TPUs used (e.g., v2 vs v3)

  • Total available RAM

  • Used disk storage

  • Machine learning frameworks used (e.g., PyTorch, Tensorflow, CNTK)

System execution times to process the entire development set must be reported.