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REAL-T

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metadata 
pretty_name: REAL-T
license: cc-by-sa-4.0
language:
  - en
  - zh
task_categories:
  - audio-to-audio
tags:
  - audio
  - speech
  - target-speaker-extraction
size_categories:
  - 1K<n<10K

REAL-T: Real Conversational Mixtures for Target Speaker Extraction

REAL-T is a conversation-centric benchmark for target speaker extraction (TSE) in real meetings and dinner-party recordings. Unlike simulated stacks such as LibriMix, the mixtures are cut from naturally overlapping speech, with enrollment clips taken from non-overlapping regions of the same conversations.

This package contains the DEV, EVAL1, and EVAL2 splits used by the REAL-TSE Challenge (a satellite challenge of IEEE SLT 2026). Official inference and scoring live in REAL-TSE-Challenge. The dataset paper is Li et al., Interspeech 2025 and Wang et al., arXiv 2026.

Task. Given a multi-speaker mixture and one or more enrollment utterances of a designated target speaker, recover that speaker’s speech. Isolated clean references are not provided (they do not exist for these real recordings). The official metrics are TER, speaker similarity, DNSMOS, and timing F1.

To run the released toolkit, copy the three split folders into the challenge repo:

cp -r REAL-T-dev REAL-T-eval1 REAL-T-eval2 /path/to/REAL-TSE-Challenge/datasets/

Then follow that repo (pre.sh, run_tse.sh, run_eval.sh). Local scoring with ground-truth transcripts is supported for DEV only.

License: CC BY-SA 4.0. See License and terms of use.

Release: v1.0.0 (2026-08-23).

Data layout

Each *_meta.csv row is one sample: one mixture + one enrollment of the target speaker, i.e. one evaluation trial. Several samples may reuse the same mixture or enrollment WAV, so the file counts in the tree below are smaller than the sample counts.

Duration and ratio cells are sample-level means.

Statistic Meaning DEV EVAL1 EVAL2
Samples Mix–enroll pairs (*_meta.csv rows); each pair is one TSE trial 1,991 2,000 3,000
Target speakers Distinct target-speaker IDs among those samples 64 49 77
Language (zh / en) Sample counts in Mandarin vs English 721 / 1,270 298 / 1,702 1,487 / 1,513
Mix duration (s) Mean length of the mixture clip 17.26 18.23 17.25
Enrollment duration (s) Mean length of the target-speaker enrollment cue 9.65 10.95 9.10
Overlap ratio Mean fraction of the mixture where two or more speakers talk at once 0.49 0.48 0.53
Target ratio Mean fraction of the mixture where the designated target speaker is active 0.75 0.74 0.73
Sources Source corpora (sample count) AISHELL-4 (240) AliMeeting (481) AMI (592) CHiME-6 (545) DiPCo (133) AliMeeting (298) AMI (948) CHiME-6 (528) DiPCo (226) unseen_CN (1,487) unseen_EN (1,513)

These map to CSV fields mixture_duration, enrolment_speakers_duration, mixture_ratio, and speaker_ratio.

DEV uses the same 1,991 mix–enroll pairs as the PRIMARY split in the Interspeech 2025 paper (same sample count and duration/ratio statistics). That is an inventory match, not a claim that the WAV files are bit-identical to a 2025 dump: the 2025 paper mixed specific microphone channels (first channel for AISHELL-4, AliMeeting, and AMI; the average of array channels for CHiME-6 and DiPCo), but the audio files of CHiME-6 & DiPCo samples in this package (SLT 2026 challenge release) are changed (because we re-selected channels and re-make the audio files).

EVAL1 is drawn from the same public corpora as DEV, with no session overlap and without AISHELL-4. AMI meeting IB4002 has a known, still-unfixed wrong headset channel mapping (Data Problems: A→3, B→2, C→0, D→1) that left its RTTM speaker labels globally permuted — for example, audio labeled MIO091 actually contains FIE038. We noticed this unfixed error after the challenge, so the released dataset differs from the dataset at REAL-TSE Challenge (SLT 2026) time.

EVAL2 is newly collected Mandarin and English conversational audio (held-out rooms and devices), including matched- and cross-channel mix–enroll pairings. Scene, channel, and pairing-condition labels for each trial are in EVAL2/scene_channel.csv (see below).

Relative to the REAL-TSE Challenge (SLT 2026) release, this package also re-normalizes every EVAL1 and EVAL2 ground_truth_transcript and the per-speaker / per-segment transcripts in the EVAL overlap JSON with the Whisper large-v2 tokenizer (English normalize, Chinese basic_normalize). DEV transcripts are left unchanged. As a result, the annotations differ from the data used at challenge time. Challenge-period EVAL1 and EVAL2 results are therefore not directly comparable to results computed on this release.

REAL-T/
├── REAL-T-dev/
│   ├── mixtures/                      # 528 wav
│   ├── enrolment_speakers/            # 242 wav
│   └── DEV/
│       ├── AISHELL-4_meta.csv
│       ├── AliMeeting_meta.csv
│       ├── AMI_meta.csv
│       ├── CHiME6_meta.csv
│       ├── DipCo_meta.csv
│       └── json/<source>/overlap_records.json
├── REAL-T-eval1/                      # same layout; no AISHELL-4
│   ├── mixtures/                      # 595 wav
│   ├── enrolment_speakers/            # 183 wav
│   └── EVAL1/
└── REAL-T-eval2/
    ├── mixtures/                      # 1,186 wav
    ├── enrolment_speakers/            # 1,913 wav
    └── EVAL2/
        ├── unseen_CN_meta.csv
        ├── unseen_EN_meta.csv
        ├── scene_channel.csv          # scene, channel, and pairing conditions
        └── json/unseen_{CN,EN}/overlap_records.json

All audio is 16 kHz, mono, 16-bit PCM WAV. The CSV fields mixture_utterance and enrolment_speakers_utterance are file stems (no .wav) under mixtures/ and enrolment_speakers/. EVAL2 mixture stems encode the recording device as H1, H2, or phone. EVAL2 enrollment stems use those same three tokens, plus A for a speaker-worn headset and external for enrollments recorded outside the meeting. mapping.csv is not shipped; the challenge toolkit regenerates it with pre.sh.

Each *_meta.csv has one row per mix–enroll pair:

Column Meaning
mixture_utterance Mixture file stem
enrolment_speakers_utterance Enrollment file stem
source Corpus or EVAL2 subset (AISHELL-4, AliMeeting, AMI, CHiME6, DipCo, unseen_CN, unseen_EN)
language zh or en
total_number_of_speaker Speakers in the mixture
speaker, gender Target speaker id and gender (M / F)
speaker_ratio Target speaker’s speaking proportion in the mixture
mixture_ratio Overlap ratio of the mixture
enrolment_speakers_duration, mixture_overlap_duration, mixture_duration Durations in seconds
ground_truth_transcript Normalized transcript of the target speaker in the mixture (used for TER)

json/<source>/overlap_records.json stores per-speaker activity in each mixture. It is used for the timing F1 metric, not as training labels.

EVAL2 scene and channel labels

EVAL2/scene_channel.csv adds recording-condition labels to the 3,000 EVAL2 trials (one row per mix–enroll pair). It joins to unseen_CN_meta.csv / unseen_EN_meta.csv on (mixture_utterance, enrolment_speakers_utterance) and does not repeat fields already in those files. Device names follow Table III of the challenge paper.

Column Meaning
mixture_scene Scene where the mixture was recorded: open_office, large_meeting_room, medium_meeting_room, small_meeting_room, restaurant, home, in-vehicle, or cafe
mix_channel Mixture recording channel: H1 (high-quality microphone), H2 (high-quality microphone, farther away), or phone
enrolment_kind direct (enrollment cut from the same meeting session) or external (separate single-speaker recording made outside the meeting)
enrol_channel Enrollment recording channel: H1, H2, phone, headset (speaker-worn close-talk), or external
enrol_scene Scene where the enrollment was recorded (same 8-value set as mixture_scene); empty for external enrollments
scene_match, device_match same / cross between mixture and enrollment for direct enrollments; empty for external enrollments (not compared)
quadrant same_scene_same_device, same_scene_cross_device, cross_scene_same_device, cross_scene_cross_device, or external

The 3,000 EVAL2 trials comprise 2,400 direct enrollments (600 per quadrant) and 600 external enrollments.

Note: these labels are provided for reference only. As discussed in the challenge paper, per-scenario metric trends do not consistently follow intuitive acoustic expectations — meeting-room recordings are not necessarily easier, and restaurant-like scenarios are not necessarily harder. This is because EVAL2 conversations were recorded in a natural, topic-driven manner with free speaker interaction, so each scenario contains substantial variability in turn-taking, overlap, speaker distance, loudness, device placement, and local noise events. The nominal scenario label alone is therefore insufficient to determine real-world TSE difficulty; scenario-wise statistics should be interpreted as a coarse diagnostic view rather than a strict ranking of acoustic difficulty.

License and terms of use

REAL-T is a public evaluation benchmark, not a training set.

  • DEV may be used for hyper-parameter selection, model comparison, and validation. Do not train, fine-tune, or run data augmentation on DEV.
  • EVAL1 and EVAL2 are for final reporting only. Do not train, fine-tune, tune hyper-parameters, or otherwise optimize on these splits.
  • At test time, process each mix–enroll pair independently. Do not adapt a model using EVAL speaker ids, session names, or other metadata.
  • Commercial use is allowed only to the extent permitted by CC BY-SA 4.0 and the original corpus licenses below.

The REAL-T package (audio clips, transcripts, and overlap annotations) is released under CC BY-SA 4.0. DEV and EVAL1 are derived from the corpora in the table; EVAL2 was recorded for this release. If you redistribute REAL-T or an adapted version of it, you must keep CC BY-SA 4.0 (or a compatible ShareAlike license), give attribution, and cite both REAL-T and every source corpus whose subset you use.

Subset in REAL-T Original corpus Original license Cite
AISHELL-4 OpenSLR 111 CC BY-SA 4.0 Fu et al., Interspeech 2021
AliMeeting OpenSLR 119 CC BY-SA 4.0 Yu et al., ICASSP 2022
AMI AMI Meeting Corpus CC BY 4.0 AMI project / Carletta 2007
CHiME-6 OpenSLR 150 CC BY-SA 4.0 Barker et al. 2018; Watanabe et al. 2020
DiPCo Amazon DiPCo CDLA-Permissive-1.0 Van Segbroeck et al., Interspeech 2020
EVAL2 Collected by the REAL-T organizers CC BY-SA 4.0 (this release) Wang et al., arXiv 2026

CHiME-5/6 were re-issued under CC BY-SA 4.0 as of 1 January 2024 (academic and commercial use; the older Sheffield paid commercial licence no longer applies). AISHELL-4 is CC BY-SA 4.0, not CC BY.

This license covers copyright in the distributed files. It does not waive speaker privacy or personality rights. Whether a model trained on ShareAlike speech is “Adapted Material” is jurisdiction-dependent; this README is not legal advice.

Source-corpus citations:

@inproceedings{fu21b_interspeech,
  title     = {{AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario}},
  author    = {Yihui Fu and Luyao Cheng and Shubo Lv and Yukai Jv and Yuxiang Kong and Zhuo Chen and Yanxin Hu and Lei Xie and Jian Wu and Hui Bu and Xin Xu and Jun Du and Jingdong Chen},
  year      = {2021},
  booktitle = {{Interspeech 2021}},
  pages     = {3665--3669},
  doi       = {10.21437/Interspeech.2021-1397},
  issn      = {2958-1796},
}

@inproceedings{Yu2022M2MeT,
  title     = {M2{M}e{T}: The {ICASSP} 2022 Multi-Channel Multi-Party Meeting Transcription Challenge},
  author    = {Fan Yu and Shiliang Zhang and Yihui Fu and Lei Xie and Siqi Zheng and Zhihao Du and Weilong Huang and Pengcheng Guo and Zhijie Yan and Bin Ma and Xin Xu and Hui Bu},
  booktitle = {{ICASSP} 2022 - 2022 {IEEE} International Conference on Acoustics, Speech and Signal Processing ({ICASSP})},
  year      = {2022},
  pages     = {6167--6171},
  doi       = {10.1109/ICASSP43922.2022.9746465},
  publisher = {{IEEE}},
}

@article{carletta2007ami,
  title   = {Unleashing the killer corpus: experiences in creating the multi-everything {AMI} Meeting Corpus},
  author  = {Jean Carletta},
  journal = {Language Resources and Evaluation},
  volume  = {41},
  number  = {2},
  pages   = {181--190},
  year    = {2007},
  doi     = {10.1007/s10579-007-9040-x},
}

@inproceedings{barker18_interspeech,
  title     = {{The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines}},
  author    = {Jon Barker and Shinji Watanabe and Emmanuel Vincent and Jan Trmal},
  year      = {2018},
  booktitle = {{Interspeech 2018}},
  pages     = {1561--1565},
  doi       = {10.21437/Interspeech.2018-1768},
  issn      = {2958-1796},
}

@inproceedings{watanabe20b_chime,
  title     = {{CHiME-6 Challenge: Tackling Multispeaker Speech Recognition for Unsegmented Recordings}},
  author    = {Shinji Watanabe and Michael Mandel and Jon Barker and Emmanuel Vincent and Ashish Arora and Xuankai Chang and Sanjeev Khudanpur and Vimal Manohar and Daniel Povey and Desh Raj and David Snyder and Aswin Shanmugam Subramanian and Jan Trmal and Bar Ben Yair and Christoph Boeddeker and Zhaoheng Ni and Yusuke Fujita and Shota Horiguchi and Naoyuki Kanda and Takuya Yoshioka and Neville Ryant},
  year      = {2020},
  booktitle = {{6th International Workshop on Speech Processing in Everyday Environments (CHiME 2020)}},
  pages     = {1--7},
  doi       = {10.21437/CHiME.2020-1},
}

@inproceedings{segbroeck20_interspeech,
  title     = {{DiPCo — Dinner Party Corpus}},
  author    = {Maarten Van Segbroeck and Ahmed Zaid and Ksenia Kutsenko and Cirenia Huerta and Tinh Nguyen and Xuewen Luo and Björn Hoffmeister and Jan Trmal and Maurizio Omologo and Roland Maas},
  year      = {2020},
  booktitle = {{Interspeech 2020}},
  pages     = {434--436},
  doi       = {10.21437/Interspeech.2020-2800},
  issn      = {2958-1796},
}

Citation

If you use REAL-T, please cite:

@inproceedings{li25da_interspeech,
  title     = {{REAL-T: Real Conversational Mixtures for Target Speaker Extraction}},
  author    = {Shaole Li and Shuai Wang and Jiangyu Han and Ke Zhang and Wupeng Wang and Haizhou Li},
  year      = {2025},
  booktitle = {{Interspeech 2025}},
  pages     = {1923--1927},
  doi       = {10.21437/Interspeech.2025-2662},
  issn      = {2958-1796},
}

@misc{wang2026slt2026realtsechallenge,
  title         = {SLT 2026 REAL-TSE Challenge: Real-world Target Speaker Extraction from Conversational Recordings},
  author        = {Shuai Wang and Zihan Qian and Ke Zhang and Jiangyu Han and Zikai Liu and Xiaoyang Yu and Haoyu Li and Marc Delcroix and Kai Yu and Lei Xie and Ming Li and Haizhou Li},
  year          = {2026},
  eprint        = {2607.15198},
  archivePrefix = {arXiv},
  primaryClass  = {eess.AS},
  url           = {https://arxiv.org/abs/2607.15198},
}

Contact: realtse.challenge@gmail.com (dataset / challenge), shuaiwang@nju.edu.cn (paper). If you find issues in the audio, labels, transcripts, or documentation, please contact us — reports are welcome and help improve the release.