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503 results for “Voice”
Adoption of Voice Assistants - Group 11
<p>This questionnaire was created for participants of the IT3010 course at NTNU. The questionnaire was created on Google Forms are distributed online.</p>
Audio from: 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model'
<p>Audio files associated with the paper 'Modeling Voiced Stop Consonants using the 3D Dynamic Digital Waveguide Mesh Vocal Tract Model', presented at the International Congress of Phonetic Sciences 2019, Melbourne, Australia.</p>
How Samsung's AI Is Improving Audio and Voice Recognition
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Thorsten-Voice Dataset 2021.06 emotional
<p>Thorsten-Voice (Thorsten-21.06-emotional) is an emotional dataset recorded by Thorsten Müller, audio optimized by Dominik Kreutz and licenced under CC0 to provide it for anybody without any financial or licence struggle.</p> <blockquote> <p><strong>"I contribute my personal voice as a person believing in a world where all people are equal. No matter of gender, sexual orientation, religion, skin color and geocoordinates of birth location. A global world where everybody is warmly welcome on any place on this planet and open and free knowledge and education is available to everyone."</strong> (Thorsten Müller)</p> </blockquote> <p>Dataset contains 2.400 recordings.</p> <ul> <li>300 sentences * 8 emotions = 2.400 recordings</li> <li>recorded by Thorsten Müller</li> <li>audio optimized by Dominik Kreutz</li> <li>mono</li> <li>samplerate 22.050Hz</li> <li>normalized to -24dB</li> <li>no silence at beginning/ending</li> <li>sentence length: 59 - 148 chars</li> </ul> <p><strong>Here's the audio length by emotion:</strong></p> <ul> <li>Neutral 🙂19 min.</li> <li>Disgusted 🤢23 min.</li> <li>Angry 😠20 min.</li> <li>Amused 😀18 min.</li> <li>Surprised 😲18 min.</li> <li>Sleepy 😔30 min.</li> <li>Drunk (<em>i was "not" drunk while recording, just pronouncing it this way!</em>) 😵25 min.</li> <li>Whispering 🤫22 min.</li> </ul> <p>For more details see my <a href="https://github.com/thorstenMueller/Thorsten-Voice">Github page</a> or <a href="https://www.Thorsten-Voice.de">Thorsten-Voice</a> project website.</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Supplementary material for "Investigating phoneme-dependencies of spherical voice directivity patterns II: Various Groups of Phonemes"
<p>The .pdf file contains</p> <ul> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> <li>additional plots</li> </ul> <p> </p> <p>The .zip files contain</p> <ul> <li>voice directivities in the SOFA format sampled on the sparse measuring grid</li> <li>voice directivities in the SOFA format upsampled to a dense grid</li> </ul> <p> </p> <p>The Matlab script provides</p> <ul> <li>an example reading a dataset, performing spatial upsampling if required, and creating some basic plots. </li> </ul>
Scrub Typhus Public Engagement in Northern Thailand - The voice of participants
<p>Scrub Typhus is a potentially deadly febrile disease that is very common in rural Southeast Asia. The population at risk has a low awareness of this disease, which complicates prevention and management. Therefore, between June and September 2020, Chiangrai Clinical Research Unit (CCRU), part of the Mahidol Oxford Tropical Medicine Research Network, together with the Primary Care Department of Chiangrai Prachanukroh Hospital carried out a series of public engagement activities in Chiangrai, northern Thailand with community health volunteers and healthcare workers in primary care units to increase disease knowledge and awareness and obtain feedback to improve future activities. Participants went on to train community members, increasing awareness and promoting preventive behaviours directly in the communities. In the video we collected interviews with participants and other stakeholders reflecting on their experience.</p>
Supplementary material for "A database for the comparison of measured datasets of human voice directivity"
<p>[1] C. Pörschmann. “A database for the comparison of measured datasets of human voice directivity," in Proceedings of the Forum Acusticum, Torino, Italy, 2023.</p> <p> </p> <p>This study presents a database that allows direct comparison and visualization of datasets from 19 different studies. The data is collected from tables, plots, and datasets from the supplemental material of the respective studies. Some studies present directivity patterns averaged over a whole sentence, while others report phoneme-dependent data. Furthermore, these datasets vary in their sampling grids, with many measured in the horizontal plane and just a few measured spherically. Most datasets included in this work present frequency-band averaged values, for example, in one-third octave bands, while a few newer studies provide the raw data in the form of transfer functions.</p> <p>Furthermore, the supplementary material contains voice directivity datasets determined over a complete sentence determined for a phonetically balanced German sentence (measured twice for 13 subjects).</p> <p> </p> <p>The .pdf file contains</p> <ul> <li>information on the database that allows comparing the results of 19 publications on voice directivities</li> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> <li>additional plots</li> </ul> <p> </p> <p>The Database.zip</p> <ul> <li>Excel-file containing datasets from the publications given in frequency-bands</li> <li>SOFA-Files (sampled on sparse grid) of all own datasets from previous studies</li> <li>Matlab scripts for importing, upsampling and visualizing all voice directivity datasets considered in the database</li> </ul> <p> </p> <p>The VoiceDirectivitySentence.zip files contain voice directivities patterns averaged over one phonetically balanced sentence in the SOFA format</p> <ul> <li>sampled on the sparse measuring grid</li> <li>upsampled to a dense grid</li> </ul>
COALA voice data and transcripts English
<p>This dataset contains audio files and transcripts in English and related to manufacturing. We collected the scripts during the Horizon Europe RIA COALA (GA 957296, <a href="https://cordis.europa.eu/project/id/957296">project reference website</a>) from industrial use cases and hired a service provider to generate the related audio files (BIBA - Bremer Institut für Produktion und Logistik GmbH ordered the service). The service provider checked the audio files for quality.</p> <p>The service provider recruited crowd workers, and gathered their audio records, informed consent (privacy) and agreement that their records become public domain (Creative Commons 0; https://creativecommons.org/share-your-work/public-domain/cc0/). The service provider declared to follow a Crowd Code of Ethics and a Fair Pay policy.</p> <p>The metadata file contains the following information:</p> <ul> <li><strong>file_name</strong>: name of the audio file</li> <li><strong>script</strong>: script the speaker had to speak</li> <li><strong>scriptId</strong>: the numeric identifier of the script</li> <li><strong>participantId</strong>: the numeric identifier of the participant (speaker)</li> <li><strong>gender</strong>: the gender as indicated by the participant (MALE or FEMALE)</li> <li><strong>age</strong>: the age in years as indicated by the participant</li> <li><strong>age_range</strong>: the age range in years (18-30, 31-45, 46+)</li> <li><strong>country</strong>: the birth country indicated by the participant</li> <li><strong>current_country</strong>: the country of residence indicated by the participant</li> <li><strong>primary_language</strong>: the language indicated as primary by the participant</li> <li><strong>ever_worked_factory</strong>: answer to the question: "Have you ever worked in a factory, manufacturing setting?" (Yes/No)</li> <li><strong>years_worked_factory</strong>: answer to the question: "If yes, for how many years?" (1-10, 10+)</li> <li><strong>background_noise_type</strong>: background noise in the audio as indicated by the participant (mild, humming/technical, no noise)</li> <li><strong>gdpr_and_ipr_consent</strong>: answer to the privacy notice and the ipr transfer to CC-0 (Yes)</li> <li><strong>date_signed</strong>: date when the participant signed the consent form (US format, MM.DD.YYYY)</li> </ul>
Repetitive Transcranial Magnetic Stimulation for "Voices"
ClinicalTrials.gov study NCT00004980. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Evaluation of Many Men, Many Voices, An STD/HIV Prevention Intervention for Black MSM
ClinicalTrials.gov study NCT00137631. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Efficacy of Familiar Voice Stimulation During Coma Recovery
ClinicalTrials.gov study NCT00557076. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Managing Insulin With a Voice AI
ClinicalTrials.gov study NCT05081011. IPD Sharing: NO. Countries: 1. Publications: 1.
Transcranial Magnetic Stimulation for "Voices"
ClinicalTrials.gov study NCT00308997. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Evaluation of the Effects of Adenoidectomy and Adenotonsillectomy on Voice in Children
ClinicalTrials.gov study NCT07399041. IPD Sharing: NO. Countries: 1. Publications: 1.
Empowering Newcomer Voices: A Trauma-Informed, Gender-Responsive Group Intervention in Schools (VOCES-NUEVAS)
ClinicalTrials.gov study NCT07041931. IPD Sharing: NO. Countries: 1. Publications: 6.
Voice Analysis as a Predictor for Difficult Intubations
ClinicalTrials.gov study NCT04105738. IPD Sharing: Not stated. Countries: 1. Publications: 5.
VOICE: Values and Options in Cancer Care
ClinicalTrials.gov study NCT01485627. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Family VOICE Study
ClinicalTrials.gov study NCT01804582. IPD Sharing: NO. Countries: 1. Publications: 1.
Voice Treatment for Parkinson's Disease
ClinicalTrials.gov study NCT03700684. IPD Sharing: YES. Countries: 1. Publications: 5.
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.