Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
859
datasets available to search
ShareScore release 0.9.0
Dataset results
859 results for “Speeches”
Dataset and documented R code for "Nouns and verbs in the speech signal"
<p>The files available constitute supplementary material to the following article:</p> <p>Lohmann, Arne. Nouns and verbs in the speech signal: Are there phonetic correlates of grammatical category? <em>Linguistics</em> - <em>An Interdisciplinary Journal of the Language Sciences</em>.</p> <p>The article is to be published online in 2020, and in 2021 in the print version of the journal.</p>
WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement
<p>This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER <a href="https://ieeexplore.ieee.org/abstract/document/8110202">Kiselev2018</a>. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). </p> <p>If you use this dataset please cite:</p> <ul> <li><strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong></li> </ul> <p>===</p> <p>Each sample is a 16-channel wav file in which the order of the channel follows the following logic:</p> <p>0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4</p> <p>Refer to the <a href="https://github.com/SensorsAudioINI/WHISPER_SET_1/blob/master/WHISPER4_floor_annotated.png">floor plan</a> for a visual illustration of the microphone arrangement.</p> <p>The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation.</p> <ul> <li>In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4)</li> <li>In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB)</li> </ul> <p>Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav).</p> <p>== </p> <p>A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on <a href="https://github.com/Enny1991/beamformers">this beamforming library</a> and <a href="https://github.com/Enny1991/sep_eval">this tool</a> to evaluate the quality of the separation.</p> <p>==</p> <p>Refer to the README.md in the dataset for more information.</p> <p>For any question please contact enea.ceolini@gmail.com</p>
The Grid Audio-Visual Lombard Speech Corpus
<p>Lombard Grid is a bi-view audiovisual Lombard speech corpus which can be used to support joint computational/behavioral studies in speech perception. The corpus includes 54 talkers, with 100 utterances per talker (50 Lombard and 50 plain utterances). This dataset follows the same sentence format as the audiovisual <a href="https://asa.scitation.org/doi/10.1121/1.5042758">Grid corpus</a>, and can thus be considered as an extension of that corpus. The sentence sets used in the Lombard Grid corpus are unique, however, and have not been utilized by the Grid corpus.</p> <p>It offers two synchronised views of the talkers (front and side) to facilitate analysis of speech from different angles. A bespoke head-mounted camera system was used to collect both front and profile views of the talkers.</p> <p><strong>Statistics</strong>: 54 talkers: 30 female talkers and 24 male talkers; 5,400 (audio, front video and side video) utterances (16,200 files in total): 50% Lombard utterances, 50% plain reference utterances.</p> <p>The dataset is described in detail in the paper,</p> <p>Najwa Alghamdi, Steve Maddock, Ricard Marxer, Jon Barker and Guy J. Brown,, "A corpus of audio-visual Lombard speech with frontal and profile views", The Journal of the Acoustical Society of America 143, El523 (2018) </p> <p>The paper is available online at <a href="http://eprints.whiterose.ac.uk/131924/">White Rose Online Research</a>.</p> <p>------------------------------------------------------------------------------------</p> <p><strong>Notes on Filenaming</strong></p> <p><strong>Filename format</strong></p> <p>SPKR_COND_UTTERANCE.wav|.mov - e.g., s8_p_sbbi9p.wav</p> <p>*SPKR = s1 to s55</p> <p>*COND = l or p, where l=> Lombard, p=> plain (i.e. non-Lombard)</p> <p>*UTTERANCE = 6-character Grid utterance code, e.g. 'pgag6a' which means 'place green at g 6 again'</p> <p><strong>Metadata format</strong></p> <p>*SPKR = s1 to s55</p> <p>*SESSION = 1 or 2</p> <p>*INDEX = 1 to 10 for ordering of the recording blocks</p> <p>*SUBINDEX = 1 to 10 for ordering of utterance in a 10-utterance block.</p> <p>*COND = l or r, where l=> Lombard, p=> plain (i.e. non-Lombard)</p> <p>*UTTERANCE = 6-character Grid utterance code, e.g. 'pgag6a' which means 'place green at g 6 again'</p> <p>If a sentence is spoken incorrectly then the filename will be</p> <p>_WRONG.wav e.g. s8_2_38_8_r_lrwizp_WRONG_lrbizp.wav</p> <p>*TRANS = the Grid utterance code for what was actually said.</p>
The airborne lifetime of small speech droplets and their potential importance to SARS-CoV-2 transmission
<p> Movies that show the experimental setup and the full 85-minute observation of speech droplet nuclei.</p> <p> </p> <p>The full movie recording of highly sensitive laser light scattering observations that indicate loud-speaking generates, in addition to hundreds of regular droplets, also many thousands of micro-droplets per second. This movie clip shows the decay of airborne particles.</p> <p> </p> <p>Note: some parts of the audio of the clips were muted for privacy.</p>
Data set for: Adjustable Deterministic Pseudonymization of Speech Listening Experiment, Report of listening experiments
<p>Data set used in "Adjustable Deterministic Pseudonymization of Speech Listening Experiment". Includes Rmarkdown script.</p> <p> </p>
Open-Unmix for Speech Enhancement (UMX SE)
<p>Weights of Open-Unmix trained on the 28-speaker version of Voicebank+Demand (Sampling rate: 16kHz). The weights can be used with <a href="https://github.com/sigsep/open-unmix-nnabla">open-unmix-nnabla</a> and <a href="https://github.com/sigsep/open-unmix-pytorch">open-unmix-pytorch</a>.</p>
The role of isochrony in speech perception in noise - Dataset
<p>This dataset contains speech stimuli and listener data reported on in Aubanel & Schwartz (2020), DOI: <a href="http://dx.doi.org/10.1038/s41598-020-76594-1">10.1038/s41598-020-76594-1</a>. </p> <p><strong>French data</strong></p> <ul> <li>French sentences are taken from the Fharvard corpus (Aubanel et al., 2020, DOI: <a href="https://dx.doi.org/10.1016/j.specom.2020.07.004">10.1016/j.specom.2020.07.004</a>)</li> <li>Speech material and sentence recordings are available at: <a href="https://dx.doi.org/10.5281/zenodo.1462854">10.5281/zenodo.1462854</a></li> <li><strong>fr_stimuli.zip</strong> contains the stimuli presented to the listeners</li> <li><strong>fr_responses.csv</strong> contains the responses typed by listeners</li> </ul> <p><strong>English data</strong></p> <ul> <li>English sentences are taken from the Harvard corpus (Rothauser et al. 1969)</li> <li>Speech material and sentence recordings are taken from the MAVA corpus, available at: <a href="https://dx.doi.org/10.4227/139/59a4c21a896a3">10.4227/139/59a4c21a896a3</a></li> <li><strong>en_stimuli.zip</strong> contains the stimuli presented to the listeners</li> <li><strong>en_responses.csv</strong> contains the responses typed by listeners</li> </ul> <p> </p>
SARS-CoV-2 transmission via speech-generated respiratory droplets
<p>The physics of generating acoustic waves involves the high-speed passage of air pressurized by the lungs through narrow passages, past the mucosal epithelial layers of the vibrating vocal folds. Sounds are further modulated by the passage of this air through narrow passages between the tongue, lips, and teeth, dislodging oral fluid at all of these locations. Generation of droplets is inevitably linked to the physics of speech generation, and not limited to one person as is highlighted in a short video recording</p>
Silent Speech EMG
<p>Facial electromyography recordings during both silent and vocalized speech.</p> <p>This data is described in the publication "Digital Voicing of Silent Speech" at EMNLP 2020 (https://arxiv.org/abs/2010.02960).</p> <p>Code for processing this data can be found at https://github.com/dgaddy/silent_speech.</p>
voiceHome-2 corpus - automatic speech recognition baseline - acoustic model
<p>This entry contains the acoustic model used for evaluation of distant-microphone speech recognition performance in:</p> <p>Nancy Bertin, Ewen Camberlein, Romain Lebarbenchon, Emmanuel Vincent, Sunit Sivasankaran, Irina Illina, Frédéric Bimbot<br> <a href="https://hal.inria.fr/hal-01923108">VoiceHome-2, an extended corpus for multichannel speech processing in real homes</a><br> <em>Speech Communication</em>, 2019, 106, pp.68-78. <a href="https://dx.doi.org/10.1016/j.specom.2018.11.002">⟨10.1016/j.specom.2018.11.002⟩</a></p>
EEG data for "Conversation electrified: ERP correlates of speech act recognition in underspecified utterances"
<p>Please refer to the publication in Plos One for a description of the experiment and data analysis: Gisladottir RS, Chwilla DJ, Levinson SC (2015) Conversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances. PLoS ONE 10(3): e0120068. doi: 10.1371/journal.pone.0120068</p>
DATA - Effects of Hearing Aid Amplification on Robust Neural Coding of Speech
<p>This data is presented in the following dissertation:<br /> Effects of Hearing Aid Amplification on Robust Neural Coding of Speech<br /> http://docs.lib.purdue.edu/open_access_dissertations/190/</p> <p>The code for analyzing this data is here:<br /> http://dx.doi.org/10.5281/zenodo.49296</p> <p>The data is organized as follows:</p> <ul> <li>The main file is Research.zip. This contains the directory structure within the "Research" folder.</li> <li>The contents of PhaseModulation.zip should be moved to "Research\Matlab"</li> <li>Everything else should be moved to "Research\Matlab\Vowel_STMP\ExpData"</li> </ul> <p> </p>
Lombard speech database for German language
<p>This is a publication of Lombard speech database for German language. Additionally, a GitHub project has been created where information about database updates and related data will be stored:</p> <p>https://github.com/Telecommunication-Telemedia-Assessment/Lombard-Speech-database.git</p> <p>For citations please use:</p> <p>Sołoducha et al., "Lombard speech database for German Language", Proc. of German Annual Conference on Acoustics (DAGA), 2016</p>
Modulating the assessment of semantic speech–gesture relatedness via transcranial direct current stimulation of the left frontal cortex
<p>Raw data related to the publication:</p> <p>Schülke, R., & <strong>Straube, B.</strong> (accepted). Modulating the assessment of semantic speech-gesture relatedness via transcranial direct current stimulation of the left frontal cortex. Brain Stimulation. DOI: 10.1016/j.brs.2016.10.012.</p> <p> </p> <p>Statistical software: SPSS</p> <p>Variables:</p> <p>Subject<br> Stimulus<br> SessionNr<br> Stimulation<br> Localisation - frontal/parietal/frontoparietal<br> Polarisation - anode left/right<br> Relatedness - related/unrelated<br> Gesture_type - iconic/metaphoric<br> Reaction_time - in milliseconds<br> Rating - on a scale from 1-7</p>
Hansard Speeches and Sentiment V1.0
<p>A public dataset of speeches in the Hansard, the record of the speeches, votes and legislation in the UK Parliament. The dataset provides information on each speech of ten words or longer, made in the House of Commons between 1980 and 2016, with information on the speaking MP, their party, gender and age at the time of the speech. The dataset also includes all speeches of ten words made from 1936 to 1979, without identifying information on the speaker.</p> <p>The speeches have been classified for sentiment using a total of five libraries from the R packages `sentimentr`, `syuzhet` and `lexicon`.</p> <p>The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented 'as is'. More details on the dataset are available at: http://evanodell.com/datasets/hansard-data/</p>
A part-of-speech (POS) lexicon of Classical Tibetan for NLP
<p>This part-of-speech (POS) lexicon of Classical Tibetan was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). The data for verbs comes from a digitized version of <em>A Lexicon of Tibetan Verb Stems as Reported by the Grammatical Tradition</em> (Munich: Bayerische Akademie der Wissenschaften, 2010) by Nathan W. Hill. Otherwise data comes from the manually part-of-speech tagged training data produced by the corpus and a few lexical items specifically added by hand to improve rule based tagging.</p>
Representations of language in a model of visually grounded speech signal: Data
<p>The set of datafiles to reproduce results from:</p> <ul> <li>Chrupała, G., Gelderloos, L., & Alishahi, A. (2017). Representations of language in a model of visually grounded speech signal. ACL. arXiv preprint: https://arxiv.org/abs/1702.01991</li> </ul>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 2)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) only on the give ENGLISH corpus. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.