Speaker Verification

VoxCeleb1 is an audio dataset containing over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.

VoxCeleb2 is a large scale speaker recognition dataset obtained automatically from open-source media. VoxCeleb2 consists of over a million utterances from over 6k speakers. Since the dataset is collected ‘in the wild’, the speech segments are corrupted with real world noise including laughter, cross-talk, channel effects, music and other sounds. The dataset is also multilingual, with speech from speakers of 145 different nationalities, covering a wide range of accents, ages, ethnicities and languages. The dataset is audio-visual, so is also useful for a number of other applications, for example – visual speech synthesis, speech separation, cross-modal transfer from face to voice or vice versa and training face recognition from video to complement existing face recognition datasets. Source: VoxCeleb2: Deep Speaker Recognition Image Source: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/

Consists of more than 210k videos for 310 audio classes. Source: VGGSound: A Large-scale Audio-Visual Dataset

CN-Celeb is a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. Source: CN-CELEB: a challenging Chinese speaker recognition dataset

The CALLHOME English Corpus is a collection of unscripted telephone conversations between native speakers of English. Here are the key details: Participants: 120 individuals. Type of Study: Naturalistic. Location: USA. Media Type: Audio. DOI: doi:10.21415/T5KP54. Contents: The corpus contains 120 telephone conversations, each lasting up to 30 minutes. Origins: All calls originated in North America, with 90 calls placed to various locations overseas and 30 calls within North America. Transcripts: The transcripts cover contiguous 5 or 10-minute segments from recorded conversations. Speaker Awareness: All speakers were aware that they were being recorded. Topics: Participants had no guidelines on what to talk about; most called family members or close friends overseas. Purpose: Collected primarily to support the project on Large Vocabulary Conversational Speech Recognition (LVCSR), sponsored by the U.S. Department of Defense.

Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and challenge series, has followed the same trend. This article provides a summary of the ASVspoof 2021 challenge and the results of 54 participating teams that submitted to the evaluation phase. For the logical access (LA) task, results indicate that countermeasures are robust to newly introduced encoding and transmission effects. Results for the physical access (PA) task indicate the potential to detect replay attacks in real, as opposed to simulated physical spaces, but a lack of robustness to variations between simulated and real acoustic environments. The Deepfake (DF) task, new to the 2021 edition, targets solutions to the detection of manipulated, compressed speech data posted online. While detection solutions offer some resilience to compression effects, they lack generalization across different source datasets. In addition to a summary of the top-performing systems for each task, new analyses of influential data factors and results for hidden data subsets, the article includes a review of post-challenge results, an outline of the principal challenge limitations and a road-map for the future of ASVspoof.

The FakeMusicCaps dataset contains total of 27605 10 seconds music tracks corresponding to almost 77 hours, generated using 5 different Text-To-Music (TTM) models. It is designed to be used as a starting dataset for the training and/or evaluation of models for the detection and attribution of synthetic music generated via TTM models. If you use this dataset, please cite our paper: Comanducci, L., Bestagini, P., and Tubaro, S., “FakeMusicCaps: a Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models”, Art. no. arXiv:2409.10684, 2024. doi:10.48550/arXiv.2409.10684.

SONICS is a large-scale dataset comprising 97,164 songs — 48,090 real songs from YouTube and 49,074 fake songs from Suno & Udio — designed for synthetic song detection (SSD), also known as fake song detection (FSD). It addresses several limitations of existing datasets, such as the lack of end-to-end fake songs, limited diversity in music-lyrics, and insufficient long-duration songs. The average length of the songs in SONICS is 176 seconds, which enables the capture of long-context relationships. Moreover, SONICS provides open access to generated fake songs and is divided into 66,709 songs for training, 26,015 songs for testing, and 4,440 songs for validation. Additionally, the inclusion of song lyrics in SONICS dataset paves the way for future research in this field.

The EVI dataset is a challenging, multilingual spoken-dialogue dataset with 5,506 dialogues in English, Polish, and French. The dataset can be used to develop and benchmark conversational systems for user authentication tasks, i.e. speaker enrolment (E), speaker verification (V), speaker identification (I). The dataset contains the audio data, machine transcriptions, and target identity for each dialogue, and the knowledge-base with personal information (postcode, name, and date of birth) for each identity. The dataset can be used with both text-independent biometric or knowledge-based authentication (KBA) tasks.

This is the forehead accelerometer variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.

This is the reference headset microphone variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.

This is the in-ear rigid earpiece-embedded microphone variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.

This is the in-ear comply foam-embedded microphone variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.

This is the temple vibration pickup variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.

This is the throat microphone (laryngophone) variant of the VibraVox dataset. VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications. This dataset, available on HuggingFace can be used for various audio machine learning tasks : - Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme) - Audio Bandwidth Extension (BWE) - Speaker Verification (SPKV) / identification - Voice cloning - etc ... The VibraVox dataset speech corpus has been released in July 2024. It includes french speech recorded simultaneously using multiple audio and vibration sensors : a forehead miniature vibration sensor, an in-ear comply foam-embedded microphone, an in-ear rigid earpiece-embedded microphone, a temple vibration pickup, a headset microphone located near the mouth, and a laryngophone. VibraVox has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.