This CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive. The newspaper texts were taken from Herald Glasgow, with permission from Herald & Times Group. Each speaker has a different set of the newspaper texts selected based a greedy algorithm that increases the contextual and phonetic coverage. The details of the text selection algorithms are described in the following paper: C. Veaux, J. Yamagishi and S. King, "The voice bank corpus: Design, collection and data analysis of a large regional accent speech database," https://doi.org/10.1109/ICSDA.2013.6709856. The rainbow passage and elicitation paragraph are the same for all speakers. The rainbow passage can be found at International Dialects of English Archive: (http://web.ku.edu/~idea/readings/rainbow.htm). The elicitation paragraph is identical to the one used for the speech accent archive (http://accent.gmu.edu). The details of the the speech accent archive can be found at http://www.ualberta.ca/~aacl2009/PDFs/WeinbergerKunath2009AACL.pdf. All speech data was recorded using an identical recording setup: an omni-directional microphone (DPA 4035) and a small diaphragm condenser microphone with very wide bandwidth (Sennheiser MKH 800), 96kHz sampling frequency at 24 bits and in a hemi-anechoic chamber of the University of Edinburgh. (However, two speakers, p280 and p315 had technical issues of the audio recordings using MKH 800). All recordings were converted into 16 bits, were downsampled to 48 kHz, and were manually end-pointed. Source: CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit (version 0.92)

LibriMix is an open-source alternative to wsj0-2mix. Based on LibriSpeech, LibriMix consists of two- or three-speaker mixtures combined with ambient noise samples from WHAM!. Source: LibriMix: An Open-Source Dataset for Generalizable Speech Separation

The WSJ0 Hipster Ambient Mixtures (WHAM!) dataset pairs each two-speaker mixture in the wsj0-2mix dataset with a unique noise background scene. It has an extension called WHAMR! that adds artificial reverberation to the speech signals in addition to the background noise. The noise audio was collected at various urban locations throughout the San Francisco Bay Area in late 2018. The environments primarily consist of restaurants, cafes, bars, and parks. Audio was recorded using an Apogee Sennheiser binaural microphone on a tripod between 1.0 and 1.5 meters off the ground.

AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for: - Kinetics (AVA-Kinetics) - a crossover between AVA and Kinetics. In order to provide localized action labels on a wider variety of visual scenes, authors provide AVA action labels on videos from Kinetics-700, nearly doubling the number of total annotations, and increasing the number of unique videos by over 500x. - Actions (AvA Actions) - the AVA dataset densely annotates 80 atomic visual actions in 430 15-minute movie clips, where actions are localized in space and time, resulting in 1.62M action labels with multiple labels per human occurring frequently. - Spoken Activity (AVA ActiveSpeaker, AVA Speech). AVA ActiveSpeaker: associates speaking activity with a visible face, on the AVA v1.0 videos, resulting in 3.65 million frames labeled across ~39K face tracks. AVA Speech densely annotates audio-based speech activity in AVA v1.0 videos, and explicitly labels 3 background noise conditions, resulting in ~46K labeled segments spanning 45 hours of data. Image Source: https://www.researchgate.net/profile/Paolo_Napoletano/publication/309327222/figure/fig1/AS:419620126248965@1477056642346/Sample-images-from-the-Aesthetic-Visual-Analysis-AVA-database-sorted-by-their-aesthetic.png

WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the speech sources in addition to the existing noise. Room impulse responses were generated and convolved using pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and further classified as high, medium, and low reverberation based on a qualitative assessment of the mixture’s noise recording.

The REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge is a benchmark for evaluation of automatic speech recognition techniques. The challenge assumes the scenario of capturing utterances spoken by a single stationary distant-talking speaker with 1-channe, 2-channel or 8-channel microphone-arrays in reverberant meeting rooms. It features both real recordings and simulated data. The challenge constis of speech enhancement and automatic speech recognition tasks in reverberant environments. The speech enhancement challenge task consists of enhancing noisy reverberant speech with single-/multi-channel speech enhancement techniques, and evaluating the enhanced data in terms of objective and subjective evaluation metrics. The automatic speech recognition challenge task consists of improving the recognition accuracy of the same reverberant speech. The background noise is mostly stationary and the signal-to-noise ratio is modest. Source: https://reverb2014.audiolabs-erlangen.de

VoiceBank+DEMAND is a noisy speech database for training speech enhancement algorithms and TTS models. The database was designed to train and test speech enhancement methods that operate at 48kHz. A more detailed description can be found in the paper associated with the database. Some of the noises were obtained from the Demand database, available here: http://parole.loria.fr/DEMAND/ . The speech database was obtained from the Voice Banking Corpus, available here: http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz .

The DNS Challenge at INTERSPEECH 2020 intended to promote collaborative research in single-channel Speech Enhancement aimed to maximize the perceptual quality and intelligibility of the enhanced speech. The challenge evaluated the speech quality using the online subjective evaluation framework ITU-T P.808. The challenge provides large datasets for training noise suppressors. Source: Deep Noise Suppression Challenge – INTERSPEECH 2020

The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning. Source: The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines

The TIMIT Acoustic-Phonetic Continuous Speech Corpus is a standard dataset used for evaluation of automatic speech recognition systems. It consists of recordings of 630 speakers of 8 dialects of American English each reading 10 phonetically-rich sentences. It also comes with the word and phone-level transcriptions of the speech. Source: Improving neural networks by preventing co-adaptation of feature detectors Image Source: https://roboticrun.wordpress.com/2016/06/21/timit-introduction-the-official-doc/

Contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. Source: AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection

The Easy Communications (EasyCom) dataset is a world-first dataset designed to help mitigate the cocktail party effect from an augmented-reality (AR) -motivated multi-sensor egocentric world view. The dataset contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head and face bounding boxes and source identification labels. We have created and are releasing this dataset to facilitate research in multi-modal AR solutions to the cocktail party problem. Source: EasyCom

VoiceBank+DEMAND is a noisy speech database for training speech enhancement algorithms and TTS models. The database was designed to train and test speech enhancement methods that operate at 48kHz. A more detailed description can be found in the paper associated with the database. Some of the noises were obtained from the Demand database, available here: http://parole.loria.fr/DEMAND/ . The speech database was obtained from the Voice Banking Corpus, available here: http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz .

L3DAS22: MACHINE LEARNING FOR 3D AUDIO SIGNAL PROCESSING This dataset supports the L3DAS22 IEEE ICASSP Gand Challenge. The challenge is supported by a Python API that facilitates the dataset download and preprocessing, the training and evaluation of the baseline models and the results submission. ## Scope of the Challenge The L3DAS22 Challenge aims at encouraging and fostering research on machine learning for 3D audio signal processing. 3D audio is gaining increasing interest in the machine learning community in recent years. The range of applications is incredibly wide, extending from virtual and real conferencing to autonomous driving, surveillance and many more. In these contexts, a fundamental procedure is to properly identify the nature of events present in a soundscape, their spatial position and eventually remove unwanted noises that can interfere with the useful signal. To this end, L3DAS22 Challenge presents two tasks: 3D Speech Enhancement and 3D Sound Event Localization and Detection, both relying on first-order Ambisonics recordings in reverberant office environments. Each task involves 2 separate tracks: 1-mic and 2-mic recordings, respectively containing sounds acquired by one 1st order Ambisonics microphone and by an array of two ones. The use of two Ambisonics microphones represents one of the main novelties of the L3DAS22 Challenge. We expect higher accuracy/reconstruction quality when taking advantage of the dual spatial perspective of the two microphones. Moreover, we are very interested in identifying other possible advantages of this configuration over standard Ambisonics formats. Interactive demos of our baseline models are available on Replicate. Top 5 ranked teams can submit a regular paper according to the ICASSP guidelines. Prizes will be awarded to the challenge winners thanks to the support of Kuaishou Technology. ## Tasks Tasks The tasks we propose are: * 3D Speech Enhancement The objective of this task is the enhancement of speech signals immersed in the spatial sound field of a reverberant office environment. Here the models are expected to extract the monophonic voice signal from the 3D mixture containing various background noises. The evaluation metric for this task is a combination of short-time objective intelligibility (STOI) and word error rate (WER). * 3D Sound Event Localization and Detection The aim of this task is to detect the temporal activities of a known set of sound event classes and, in particular, to further locate them in the space. Here the models must predict a list of the active sound events and their respective location at regular intervals of 100 milliseconds. Performance on this task is evaluated according to the location-sensitive detection error, which joins the localization and detection error metrics. ## Dataset Info The L3DAS22 datasets contain multiple-source and multiple-perspective B-format Ambisonics audio recordings. We sampled the acoustic field of a large office room, placing two first-order Ambisonics microphones in the center of the room and moving a speaker reproducing the analytic signal in 252 fixed spatial positions. Relying on the collected Ambisonics impulse responses (IRs), we augmented existing clean monophonic datasets to obtain synthetic tridimensional sound sources by convolving the original sounds with our IRs. We extracted speech signals from the Librispeech dataset and office-like background noises from the FSD50K dataset. We aimed at creating plausible and variegate 3D scenarios to reflect possible real-life situations in which sound and disparate types of background noises coexist in the same 3D reverberant environment. We provide normalized raw waveforms as predictors data and the target data varies according to the task. The dataset is divided in two main sections, respectively dedicated to the challenge tasks. The first section is optimized for 3D Speech Enhancement and contains more than 60000 virtual 3D audio environments with a duration up to 12 seconds. In each sample, a spoken voice is always present alongside with other office-like background noises. As target data for this section we provide the clean monophonic voice signals. For each subset we also provide a csv file, where we annotated the coordinates and spatial distance of the IR convolved with the target voice signals for each datapoint. This may be useful to estimate the delay caused by the virtual time-of-flight of the target voice signal and to perform a sample-level alignment of the input and ground truth signals. The other sections, instead, is dedicated to the 3D Sound Event Localization and Detection task and contains 900 30-seconds-long audio files. Each data point contains a simulated 3D office audio environment in which up to 3 simultaneous acoustic events may be active at the same time. In this section, the samples are not forced to contain a spoken voice. As target data for this section we provide a list of the onset and offset time stamps, the typology class, and the spatial coordinates of each individual sound event present in the data-points. We split both dataset sections into a training set and a development set, paying attention to create similar distributions. The train set of the SE section is divided in two partitions: train360 and train100, and contain speech samples extracted from the correspondent partitions of Librispeech (only the sample) up to 12 seconds. The train360 is split in 2 zip files for a more convenient download. All sets of the SELD section are divided in: OV1, OV2, OV3. These partitions refer to the maximum amount of possible overlapping sounds, which are 1, 2 or 3, respectively. ## L3DAS22 Challenge Supporting API The gitHub supporting API is aimed at downloading the dataset, pre-processing the sound files and the metadata, training and evaluating the baseline models and validating the final results. We provide easy-to-use instruction to produce the results included in our paper. Moreover, we extensively commented our code for easy customization. For further information please refer to the challenge website and to the challenge documentation.

The EARS-WHAM dataset mixes speech from the EARS dataset with real noise recordings from the WHAM! dataset. Speech and noise files are mixed at signal-to-noise ratios (SNRs) randomly sampled in a range of [−2.5, 17.5] dB, where the SNR is computed using loudness K- weighted relative to full scale (LKFS) standardized in ITU-R BS.1770 to obtain a more perceptually meaningful scaling and also to remove silent regions from the SNR computation.

The QMUL underGround Re-IDentification (GRID) dataset contains 250 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views. All images are captured from 8 disjoint camera views installed in a busy underground station. The figures beside show a snapshot of each of the camera views of the station and sample images in the dataset. The dataset is challenging due to variations of pose, colours, lighting changes; as well as poor image quality caused by low spatial resolution. Source: https://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html

L3DAS21 is a dataset for 3D audio signal processing. It consists of a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. The LEDAS21 datasets contain multiple-source and multiple-perspective B-format Ambisonics audio recordings. The authors sampled the acoustic field of a large office room, placing two first-order Ambisonics microphones in the center of the room and moving a speaker reproducing the analytic signal in 252 fixed spatial positions. Relying on the collected Ambisonics impulse responses (IRs), the authors augmented existing clean monophonic datasets to obtain synthetic tridimensional sound sources by convolving the original sounds with our IRs. The dataset is divided in two main sections, respectively dedicated to the challenge tasks. The first section is optimized for 3D Speech Enhancement and contains more than 30000 virtual 3D audio environments with a duration up to 10 seconds. In each sample, a spoken voice is always present alongside with other office-like background noises. As target data for this section the authors provide the clean monophonic voice signals. The other sections, instead, is dedicated to the 3D Sound Event Localization and Detection task and contains 900 60-seconds-long audio files. Each data point contains a simulated 3D office audio environment in which up to 3 simultaneous acoustic events may be active at the same time. In this section, the samples are not forced to contain a spoken voice. As target data for this section the authors provide a list of the onset and offset time stamps, the typology class, and the spatial coordinates of each individual sound event present in the data-points. Source: L3DAS21

The Audio Signal and Information Processing Lab at Westlake University, in collaboration with AISHELL, has released the Real-recorded and annotated Microphone Array speech&Noise (RealMAN) dataset, which provides annotated multi-channel speech and noise recordings for dynamic speech enhancement and localization: - Microphone array: A 32-channel microphone array with high-fidelity microphones is used for recording - Speech source: A loudspeaker is used for playing source speech signals (about 35 hours of Mandarin speech) - Recording duration and scene: A total of 83.7 hours of speech signals (about 48.3 hours for static speaker and 35.4 hours for moving speaker) are recorded in 32 different scenes, and 144.5 hours of background noise are recorded in 31 different scenes. Both speech and noise recording scenes cover various common indoor, outdoor, semi-outdoor and transportation environments, which enables the training of general-purpose speech enhancement and source localization networks. - Annotation: To obtain the task-specific annotations, speaker location is annotated with an omni-directional fisheye camera by automatically detecting the loudspeaker. The direct-path signal is set as the target clean speech for speech enhancement, which is obtained by filtering the source speech signal with an estimated direct-path propagation filter.

The EARS-Reverb dataset uses real recorded room impulse responses (RIRs) from multiple public datasets (ACE-Challenge, AIR, ARNI, BRUDEX, dEchorate, DetmoldSRIR, and Palimpsest). All RIRs are fullband, and a randomly selected channel for multi-channel recordings is used. The reverberant speech is generated by convolving the clean speech with the RIR. To avoid a time delay between the reverberant and clean speech signal caused by the direct path of the RIR, the beginning of the RIR is cut off up to the index with the highest amplitude. Only RIRs with an RT60 reverberation time that does not exceed 2 s are used. Finally, the loudness of the reverberant speech is normalized to the loudness of the clean speech using the loudness K-weighted relative to full scale (LKFS).

Noiseless reverberant dataset using the public WSJ0 corpus and simulated room impulse responses using the PyRoomAcoustics library. Used in: - Speech Enhancement and Dereverberation with Diffusion-based Generative Models, Richter et al., arXiv 2022 - StoRM: A Stochastic Regeneration Model for Speech Enhancement and Dereverberation, Lemercier et al., arXiv 2022 - Analysing Discriminative versus Diffusion-based Generative Models for Speech Restoration, Lemercier et al., ICASSP 2023

A new large-scale, in-thewild Mandarin dataset, CAS-VSR-S101 with 101.1 hours of data. The videos are sourced from broadcast news and conversational programs in Chinese, covering a highly diverse set of topics, speakers and filming conditions. The lengths of the utterances are naturally distributed between 0.01s and 10.57s, and image qualities and resolutions vary. News accounts for 82.4% of the programs. 70.4% of the utterances depict news anchors, hosts and correspondents, while 29.6% are those of interviewees and guests. In addition, at a ratio of approximately 1.5 : 1, male and female appearances are relatively balanced. It is divided into train, validation and test sets by TV channels to minimize speaker overlap, and at a ratio of roughly 8 : 1 : 1.5 in terms of duration. The validation and test sets are composed of programs broadcast on provincial TV channels. The dataset is available for academic use under a license.

A Brazilian Portuguese TTS dataset featuring a female voice recorded with high quality in a controlled environment, with neutral emotion and more than 20 hours of recordings. with neutral emotion and more than 20 hours of recordings. Our dataset aims to facilitate transfer learning for researchers and developers working on TTS applications: a highly professional neutral female voice can serve as a good warm-up stage for learning language-specific structures, pronunciation and other non-individual characteristics of speech, leaving to further training procedures only to learn the specific adaptations needed (e.g. timbre, emotion and prosody). This can surely help enabling the accommodation of a more diverse range of female voices in Brazilian Portuguese. By doing so, we also hope to contribute to the development of accessible and high-quality TTS systems for several use cases such as virtual assistants, audiobooks, language learning tools and accessibility solutions. Possible use cases: TTS; Voice Conversion; ASR; Speech Enhancement

A database containing high sampling rate recordings of a single speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for speech synthesis and automatic speech recognition applications, the dataset contains text extracted from a popular Brazilian news TV program, totalling roughly 20 h of audio spoken by a trained individual in a controlled environment. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. Intended for: TTS ASR Speech Enhancement Voice Conversion

We present a multilingual test set for conducting speech intelligibility tests in the form of diagnostic rhyme tests. The materials currently contain audio recordings in 5 languages and further extensions are in progress. For Mandarin Chinese, we provide recordings for a consonant contrast test as well as a tonal contrast test. Further information on the audio data, test procedure and software to set up a full survey which can be deployed on crowdsourcing platforms is provided in our paper [arXiv preprint] and GitHub repository. We welcome contributions to this open-source project.

The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. Each file is labelled with subjective ratings of the overall quality and the quality dimensions Noisiness, Coloration, Discontinuity, and Loudness. In total, it contains more than 97,000 human ratings for each of the dimensions and the overall MOS. The NISQA Speech Quality Corpus contains two training, two validation and four test datasets: - NISQA_TRAIN_SIM and NISQA_VAL_SIM: contains simulated distortions with speech samples from four different datasets. Divided into a training and a validation set. - NISQA_TRAIN_LIVE and NISQA_VAL_LIVE: contains live phone and Skype recordings with Librivox audiobook samples. Divided into training and validation set. - NISQA_TEST_LIVETALK: contains recordings of real phone and VoIP calls. - NISQA_TEST_FOR: contains live and simulated conditions with speech samples from the forensic speech dataset. - NISQA_TEST_NSC: contains live and simulated conditions with speech samples from the NSC dataset. - NISQA_TEST_P501: contains live and simulated conditions with speech samples from ITU-T Rec. P.501. The datasets are provided under the original terms of the used source speech and noise samples. Please see the individual readme and license files in each of the dataset folders within the NISQA_Corpus.zip for more details about the datasets and the licenses. Generally, all of the files in this corpus can be used for non-commercial research purposes and some of the datasets can be also be used for commercial purposes.

Uses same clean speech as VoiceBank+Demand but more noise types. Features much lower SNRs ([−10, −5, 0, 5, 10, 15, 20] dB) compared to VoiceBank+Demand and has babble and speech shaped noise in both train, validation, and test set.

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 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.

WHAMR_ext is an extension to the WHAMR corpus with larger RT60 values (between 1s and 3s)