Datasets:

Nanboy
/

RVCBench

We use a curated subset of the CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92) Yamagishi et al., 2019.

The original VCTK corpus contains ~44 hours of English read speech from 110 speakers with diverse accents (American, British, Irish, Scottish, Indian, etc.), each reading about 400 phonetically rich sentences recorded with high-quality microphones in a hemi-anechoic chamber.

In our benchmark, we select 40 speakers to balance gender and accent while keeping the dataset compact:

Speakers: 40 in total (22 female, 18 male)
Accents: 12 accent categories
- Major accents (American, English, Canadian, Irish, Scottish, South African) have 4–6 speakers each
- Minor accents (e.g., Australian, New Zealand, Welsh, Northern Irish, Indian, British RP) have 1–3 speakers each

All audio is used at 48 kHz, 16-bit PCM, mono, following the original VCTK speaker IDs (e.g., p294, p303, s5).

LibriTTS (English multi-speaker TTS)

We use a small, speaker-balanced subset of the LibriTTS corpus. LibriTTS is a large-scale, multi-speaker English corpus of read audiobooks, derived from LibriSpeech and released at 24 kHz for TTS research: it contains approximately 585 hours of speech from 2,456 speakers, with sentence-level segmentation, normalized text, and noisy utterances removed.

Official LibriTTS page: https://www.openslr.org/60/

In our benchmark subset:

Speakers: 40 total, all from LibriTTS dev and test splits
Gender balance (overall):
- 20 female speakers
- 20 male speakers
Split design (speaker-disjoint):
- dev: 19 speakers (7 F / 12 M)
- test: 21 speakers (13 F / 8 M)

All audio is used at 24 kHz, mono, 16-bit PCM, following the original LibriTTS speaker IDs

AISHELL-1

We use a subset of the AISHELL-1 Mandarin corpus for development and evaluation.
AISHELL-1 is an open-source Mandarin speech corpus containing about 178 hours of transcribed speech from 400 speakers, recorded in quiet indoor environments and released at 16 kHz. The texts cover 11 application-oriented domains (finance, science & technology, sports, entertainment, news, etc.).

Official AISHELL-1 page: https://www.openslr.org/33/

In our benchmark subset:

Speakers: 40 total (IDs S0724–S0763)
Gender balance: 28 female, 12 male
Per-speaker content: 50 paired utterances (gt, ori) per speaker
Sampling rate: 16 kHz, mono, 16-bit PCM

UEDIN_bilingual_96kHz (Mandarin/English bilingual)

We use the Mandarin talkers portion of the 96 kHz release of the EMIME Bilingual English–Mandarin Database.
The original EMIME Mandarin/English database contains studio recordings of 14 native Mandarin speakers (7 female, 7 male) who read parallel material in both Mandarin and English for research on personalized speech-to-speech translation and cross-lingual voice conversion. Official EMIME bilingual database page: https://www.emime.org/participate/emime-bilingual-database.html

In our benchmark subset:

Speakers: 13 total from Mandarin_talkers/
- Female: 6 (MF1, MF3, MF4, MF5, MF6, MF7)
- Male: 7 (MM1–MM7)
- We exclude MF2 because some of her Mandarin recordings exhibit abnormal behaviour.
Gender balance: 6 female, 7 male
Bilingual content per speaker:
- 25 English → Mandarin sentence pairs
- 25 Mandarin → English sentence pairs
  → 50 bilingual pairs per speaker
Sampling rate: 96 kHz, mono, 16-bit PCM

CommonVoiceFR_dev (French crowd-sourced speech)

We use a subset of Mozilla Common Voice French v23.0, taking clips from the validated.tsv split.

Official Common Voice site: https://commonvoice.mozilla.org/

In our benchmark subset:

Source split: validated.tsv from Common Voice French v23.0
Speakers: 40 unique French speakers
Per-speaker content: 50 utterance pairs per speaker.
Audio format: mono, 16-bit PCM, 48 kHz (downsampled in code if needed)

Long_context (LibriSpeech-Long subset)

We use a long-form English subset derived from LibriSpeech-Long, a benchmark dataset for long-form speech processing released with “Long-Form Speech Generation with Spoken Language Models” (Park et al., 2024).

Original LibriSpeech-Long card: https://huggingface.co/datasets/ilyakam/librispeech-long

In our benchmark subset (“Long_context”):

Language: English
Speakers: 10
- Speaker IDs: 1272, 1673, 1919, 1993, 2078, 3576, 3853, 422, 6241, 8842
Pairs per speaker: 2 long-form utterance pairs (e.g., original vs processed)
Total pairs: 20
Audio duration: long-form segments, typically 0.4–4 minutes per file
Sampling rate: 16 kHz, mono, 16-bit PCM (inherited from LibriSpeech)

VCTK natural_noise (VoiceBank+DEMAND subset)

We use a noisy VCTK subset derived from the “Noisy speech database for training speech enhancement algorithms and TTS models” (also known as the VoiceBank+DEMAND dataset).
The original database provides parallel clean/noisy speech at 48 kHz based on the VCTK multi-speaker corpus, where clean VCTK utterances are mixed with environmental noises (mainly from the DEMAND corpus) and additional speech-shaped / babble noise, and is widely used for speech enhancement and noise-robust TTS research.

Official dataset page: https://datashare.ed.ac.uk/handle/10283/2791

In our benchmark subse:

Language: English (multi-accent, inherited from VCTK)
Speakers: 20 VCTK speakers (p226–p273)
Background noise: 10 natural noise environments, all mixed at 10 dB SNR
- babble, cafeteria, car, kitchen, meeting, metro, restaurant,
  ssn (speech-shaped noise), station, traffic
Per-speaker structure:
- 20 noisy–clean pairs (each noisy utterance has a clean VCTK reference)
- 10 additional clean-only utterances
- → 30 sentence-level items per speaker
Overall size: 20 speakers × 30 items ≈ 600 sentence items (clean/noisy together give about 1,200 audio files)
Audio format: 48 kHz, mono, 16-bit PCM

Multispeaker_libri (English multi-speaker interference)

We use a synthetic multi-speaker interference set built from our LibriTTS English audiobook subset.
Clean target utterances from 10 LibriTTS speakers are mixed with interfering speech from 2 additional speakers at controlled SNR levels, producing parallel clean / mixture pairs for studying multi-speaker robustness.

In our benchmark subset:

Target speakers: 10 (5 male, 5 female)
- Male: 61, 908, 2300, 2830, 7729
- Female: 237, 1221, 1284, 4970, 6829
Interferer speakers: 2
- Female interferer: 121
- Male interferer: 672
Clean groundtruth segments: 100 (10 per target speaker)
Mixtures per groundtruth: 8
- 4 SNR levels: -5 dB, 0 dB, +5 dB, +10 dB
- 2 interferers: 121, 672
Total entries in manifest.json: 800 mixture entries (each with a linked clean groundtruth)
Sampling rate: 16 kHz, mono, 16-bit PCM

iemocap (TO BE EXPLORED — emotional English speech)

Status: to be explored — this dataset is prepared but not yet integrated into the main benchmark pipeline.

We construct an emotional subset from the IEMOCAP (Interactive Emotional Dyadic Motion Capture) corpus.

In our prepared subset:

Language: English
Speakers: 10 total (5 male, 5 female)
- Female: Ses01_F, Ses02_F, Ses03_F, Ses04_F, Ses05_F
- Male: Ses01_M, Ses02_M, Ses03_M, Ses04_M, Ses05_M
Total pairs: 184 (ori, gt) audio pairs
Emotion categories (6): ang, exc, fru, hap, neu, sad

Pair construction rule

For each speaker, we select utterances from the six emotion types and build pairs such that:

ori and gt always come from the same speaker, but
their emotions are deliberately different (e.g., ori = ang, gt = neu).

robocall_ftc (TO BE DONE — real-world scam calls)

Status: to be done — planned for trustworthy deepfake / telecom-fraud experiments.

We plan to integrate the Robocall Audio Dataset released by Prasad & Reaves (NCSU) based on the FTC’s Project Point of No Entry (PPoNE).
This dataset contains real-world audio recordings of automated or semi-automated phone calls (“robocalls”), most of which are suspected to be illegal scam or spam calls.

Official repo: https://github.com/wspr-ncsu/robocall-audio-dataset

Planned usage in our benchmark: