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.
28
datasets available to search
ShareScore release 0.9.0
Dataset results
28 results for “vowel”
Japanese Vowels
<p>This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers. The dataset consists of multivariate time series (12 variables) of length 29 time steps. Each time series is associated with a class (1-9) indicating the speaker. The dataset consists of 270 time series used for training and 370 for testing.</p> <p>This is a pre-processed version saved in numpy format. The original Japanese Vowels dataset and additional information can be found on <a href="https://archive.ics.uci.edu/dataset/128/japanese+vowels">UCI</a>.</p> <p>The data are 3-dimensional arrays of shape [n_samples, time_steps, n_variables]. The data can be loaded as follows:</p> <pre><code>loaded_data = np.load("Japanese_Vowels.npz") Xtr = loaded_data['Xtr'] # Training data of shape (270, 29, 12) Ytr = loaded_data['Ytr'] # Training labels of shape (270, 1) Xte = loaded_data['Xte'] # Test data of shape (370, 29, 12) Yte = loaded_data['Yte'] # Test labels of shape (370, 1)</code></pre>
Audio files of synthetic sustained vowels for the study of vocal fry
<p>This is the dataset of stimuli used in the experiments reported in [1]. SinglePulsing.zip contains the stimuli of reported experiment 1, Transition.zip contains stimuli of experiment 2.</p> <p></p> <p>[1] V. Devaraj, F. Wendt, I. Roesner, J. Schoentgen, and P. Aichinger, “Auditory perception of impulsiveness and tonality in vocal fry,” <em>Appl. Sci. Basel</em>. (under review)</p>
French vowels
<p>Dataset of audio recordings.</p> <p>20 adult French speakers are recorded saying 8 vowels, under 7 conditions, 10 times each.</p> <p> </p> <p>There are 15 women and 5 men.</p> <p>Each individual prononces 8 vowels</p> <pre><code class="language-html">[&#97;&#771;&#601;&#105;&#111;&#596;&#117;&#121]</code></pre> <p>in 7 conditions: natural, low voice, high voice, short, long, on an ascending and descending scale.</p> <p>There are 10 utterances for each condition.</p> <p> </p> <p>The records have been made with a Zoom H6 recorder, using the included stereo mic XYH-6. The sampling rate of the recordings is 44100 Hz, 16 bits.</p> <p> </p>
Stable Vowel Corpus (SVC): a benchmark for spectral stability analyses
<p>This dataset comprises 4,860 synthesized CVC-like sequences generated using Praat's articulatory synthesizer (Boersma, 1998; Boersma & Weenink, 2021). The duration and location of the spectrally stable portion of the vowel within these CVC-like sequences were randomly varied.</p><p>Each corresponding .wav file is labeled with the following format:</p><ul><li><strong>Stimulus</strong> <i>ID_TsV_TeV_C1_V_C2_speaker.wav</i></li><li><strong>e.g.</strong> <i>1000_0.2197529077064245_0.2906336685046843p_a_d_female.wav</i></li></ul><p>Here's the breakdown of the naming convention:</p><ul><li><strong>Stimulus ID</strong>: A unique identifier for each stimulus.</li><li><strong>TsV</strong>: The timecode of the start of the stable portion of the vowel.</li><li><strong>TeV</strong>: The timecode of the end of the stable portion of the vowel.</li><li><strong>C1</strong>: The first consonant category.</li><li><strong>V</strong>: The vowel category.</li><li><strong>C2</strong>: The second consonant category.</li><li><strong>Speaker</strong>: The type of artificial speaker used for the synthesis.</li></ul><p>Further details about the design and synthesis of the corpus have been published in:</p><ul><li>Genette, J., Rivera Espejo, J. M., Gillis, S., & Verhoeven, J. (2023). Determining spectral stability in vowels: A comparison and assessment of different metrics. <i>Speech Communication</i>, <i>154</i>, 102984. <a href="https://doi.org/10.1016/j.specom.2023.102984">https://doi.org/10.1016/j.specom.2023.102984</a></li></ul><h4><strong>References</strong></h4><p>Boersma, P. (1998). <i>Functional Phonology: Formalizing the Interactions between Articulatory and Perceptual Drives</i> [PhD Thesis]. University of Amsterdam.</p><p>Boersma, P., & Weenink, D. (2021).<i> Praat: doing phonetics by computer</i> [Computer program. Version 6].</p>
Ndaka [ndk] Contrastive Vowels
<p>This is a set of sound files exemplifying the nine Ndaka [ndk] contrastive vowels.</p>
Young and older adult vowel categorization responses
<p>Age-related changes in auditory processing may reduce physiological coding of acoustic cues, contributing to older adults' difficulty perceiving speech in background noise. This study investigated whether older adults differed from young adults in patterns of acoustic cue weighting for categorizing vowels in quiet and in noise. All participants relied primarily on spectral quality to categorize /Ꜫ/ and /æ/ sounds in both listening conditions. However, relative to young adults, older adults exhibited greater reliance on duration and less reliance on spectral quality. These results suggest that aging alters patterns of perceptual cue weights that may influence speech recognition abilities.</p>
PDF and PSD files of DiapixGEtv picture materials – German version adapted to elicit tense vowels
<p>This zipped folder contains PDF and PSD files of our translation of the DiapixUK picture materials by Baker & Hazan (2011) - adapted to elicit tense vowels in German (DiapixGEtv).</p> <p>For our purpose we only modified the written information when making adjustments to the original DiapixUK versions of the files. Our aim was to include as many tense vowels [i:, e:, a:, o:, u:] as possible while remaining subtle enough so as not to alert the participants to our research focus . The additional documentation contains information on the translation process and an overview over the adapted text parts containing target vowels in stressed syllables including the target word with its target vowel and pronunciation as well as its meaning in English.</p> <p>In order to facilitate further adaptation/modification each item in the PSD files is in a separate layer.</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB 1412, 416591334</p>
AcousticsAustraliaArticle_dataset: audio samples _ vowel phonation
<p><strong>PaperAcousticsAustralia - data</strong></p> <p> </p> <p>Audio samples were collected from 31 participants, extracted from their recorded reading (31 participants), recorded interviews (28 participants) and vowel phonations (29 participants).</p> <p><strong>ParticipantProfiles spreadsheet</strong> details the age, gender, years smoking and ethnicity per participant.</p> <p> <strong>ReadingsAudioSamples : </strong>audio samples extracted from recorded readings. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples extracted for each participant.</p> <p> <strong>InterviewsAudioSamples :</strong> audio samples extracted from recorded interviews. This concerns 28 participants: there is no audio samples available from the interviews of participants P20, P33 and P42. There are generally at least two audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the participant’s audio samples.</p> <p> <strong>VowelPhonationAudioSamples :</strong> audio samples of vowel /a/ sustained phonation for 29 participants. Most samples were recorded by the participants, and eight of them were recorded by phone (look for ‘phone’ in the audio sample name). There is only one vowel phonation sample per participant and no vowel phonation sample was provided for participants P16 and P25. </p>
RAW DATA form - Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs
<p><strong>Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs</strong></p> <p>Ghada BinKhamis, Agnès Léger, Steven L. Bell, Garreth Prendergast, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>doi: 10.1097/AUD.0000000000000648</strong></p> <p><em>(<strong>Please site above article)</strong></em></p> <p> </p> <p><strong>Description of raw EEG (speech-ABR) data main folder, subfolders, and raw EEG files</strong></p> <p><strong>Folder Information</strong></p> <p><strong>Main folder:</strong></p> <ul> <li>Contains 144 subfolders with raw data from 12 participants</li> </ul> <p><strong>Subfolder names:</strong></p> <ul> <li>Each subfolder starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12</li> </ul> </li> </ul> <ul> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>And finally the background condition <ul> <li>quiet, noise</li> </ul> </li> </ul> <p><strong>Example subfolder names:</strong></p> <ul> <li><em>S01 40ms da noise:</em>Participant number 1, speech-ABRs in response to the 40ms [da] in background noise</li> <li><em>S07 170ms ga quiet:</em>Participant number 7, speech-ABRs in response to the 170ms [ga] in quiet</li> </ul> <p><strong>Each participant has 12 subfolders:</strong></p> <ol> <li>S__ 40ms da quiet </li> <li>S__ 40ms da noise</li> <li>S__ 50ms da quiet</li> <li>S__ 50ms da noise</li> <li>S__ 50ms ba quiet</li> <li>S__ 50ms ba noise</li> <li>S__ 50ms ga quiet</li> <li>S__ 50ms ga noise</li> <li>S__ 170ms da quiet</li> <li>S__ 170ms da noise</li> <li>S__ 170ms ba quiet</li> <li>S__ 170ms ga quiet</li> </ol> <p><strong>Each participant subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong></p> <ul> <li>Each ‘.mat’ file starts with the participant code <ul> <li>S01, S02, S03, S04, S05, S06, S07, S08, S09, S10, S11, S12 </li> </ul> </li> <li>Next is the stimulus duration: <ul> <li>40ms, 50ms, 170ms</li> </ul> </li> <li>Next is the CV used to evoke speech-ABRs <ul> <li>ba, da, ga</li> </ul> </li> <li>Next is ‘noise’ if background condition was noise</li> <li>Next is the stimulus polarity <ul> <li>Pos for positive/standard</li> <li>Neg for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally is the recording number for that polarity <ul> <li>R1 is the first recording</li> <li>R2 is the second recording</li> </ul> </li> </ul> <p><strong>Example ‘.mat’ file name:</strong></p> <ul> <li><em>S04 50 ba Neg R1:</em>Participant number 4, speech-ABR in response to the 50ms [ba] in quiet, reversed polarity stimulus, recording number one </li> <li><em>S02 40 da noise Pos R2:</em>Participant number 2, speech-ABR in response to the 40ms [da] in background noise, standard/positive stimulus, recording number two</li> </ul> <p> </p> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example: <ul> <li>xunits: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li>start: ‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li>points: <strong>1800</strong>is the number of sample points for speech-ABRs to the 40ms da, this number will be <strong>2200</strong>for the speech-ABRs to the 50ms stimuli (ba, da, ga), and <strong>4600</strong>for the speech-ABRs to the 170ms stimuli (ba, da, ga)</li> <li>chans: 2 is the number of channels (channel 2 is the ipsilateral channel)</li> <li>frames: 3000 is the number of epochs</li> </ul> </li> <li>The last filed <strong>‘values’</strong>is what contains the raw EEG data (1800x2x3000) <ul> <li><strong>1800 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>3000 </strong>is the number of epochs</li> <li>The field <strong>‘values’ </strong>for speech-ABRs to the 50ms stimuli is <strong>2200x2x3000 </strong>and for speech-ABRs to the 170ms stimuli is <strong>4600x2x3000</strong>.</li> </ul> </li> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> </ul> <p><strong>Data is recorded in Volts and will need to be converted to Micro Volts</strong></p> <p><strong>Date of data collection</strong>: May to November 2016</p>
vowel formant representation in human speech cortex
<p>Data accompanying the paper publication.</p>
Young and older adult vowel categorization responses
Open the record for dataset details and reuse information.
Data from: Dogs perceive and spontaneously normalise formant-related speaker and vowel differences in human speech sounds
Open the record for dataset details and reuse information.
More Austro-Tai Comparisons and Observations on Vowel Correspondences
<p>In this presentation, 13 new comparisons between Kra-Dai and Austronesian are presented. The comparisons include terms like ‘rattan’, ‘to plant or transplant crops’, ‘derris root used as fish poison’, to be sick; in pain', ‘leech’, and other basic vocabulary terms. In addition, observations on vowel correspondences between final-syllable vowels are discussed. High-vowels in the penultimate syllable caused a split in reflexes of *a. Additionally, there was an unconditioned split affecting schwa, which appears in Kra-Dai and Austronesian examples for which several examples are given.</p>
Korean ideophonic reduplicatives sorted by their vowel harmony patterns
<p>This is a list of 4,024 di- and tri-syllabic stem-based ideophonic reduplicatives (e.g., <em>culəŋ-culəŋ</em> ‘in clusters’, <em>ucik’ɨn-ucik’ɨn</em> ‘with a snap, crackling’), as a core of the ideophonic lexicon, extracted from a written corpus of 29,015 Korean ideophones (http://www.hangeul.pe.kr/symbol/words.htm). </p>
Analyzing Acoustic Properties of the Vowel "a" Collected Via Mobile Phone From Individuals Diagnosed With Chronic Obstructive Pulmonary Disease and Health Control Groups
ClinicalTrials.gov study NCT06705647. IPD Sharing: NO. Countries: 1. Publications: 1.
Vowel Segmentation for Classification of Chronic Obstructive Pulmonary Disease Using Machine Learning
ClinicalTrials.gov study NCT06160674. IPD Sharing: NO. Countries: 1. Publications: 1.
Nepali Sign Language -Consonant and Vowel
<p>Nepali Sign language is used by the differently able Nepali speaking community of Sikkim, hilly regions of North Bengal, some parts of Uttrakhand, Meghalaya and Assam in India and the major part of Nepal. </p><p>This dataset is the first Nepali Sign language dataset prepared to represent consonant and vowel signs in the form of videos.</p><p>Dataset was collected with the help of 14 volunteers, categorized as 5 Native (>25 years and >5 years of using Nepali Sign Language) and 9 beginners learning Nepali Sign Language.</p><p>Dataset has 630 total videos containing 1205 gestures for 36 consonants and 13 vowels of Nepali Sign Language out of which 516 Videos is for consonant that has 972 gestures and 114 videos for vowel that has 234 gestures. </p><p>Data was collected in two types of environment </p><ol><li><strong>Prepared environment</strong> - Black background by putting a black chart paper at the background of the wall where the gesture is performed.</li><li><strong>Unprepared environment</strong>- No black chart paper was used to change the background while performing the gesture.</li></ol><p> Real world environment indoor and outdoor has also been considered.</p><p><strong>Lighting condition</strong> – Natural lighting condition has been considered while capturing the data at indoor and outdoor i.e no extra light was focused on the volunteer performing the gesture. Bright and Dark condition for indoor videos are referring the videos captured by turning the room lights and off respectively.</p>
THE PHONETIC SYSTEM OF UZBEK, FINNISH, ENGLISH AND DIFFERENCES BETWEEN THE VOWEL SOUNDS OF THIS THREE LANGUAGES.
Open the record for dataset details and reuse information.
Supplementary data to Shuster, Lawrence, and Yannick Wey: Mapping vowel color and morphology
<p>Supplementary data to: Shuster, Lawrence, and Yannick Wey. 2021. "Mapping vowel color and morphology: A cross-cultural analysis of vocal timbres in four yodeling traditions." <em>AAMW Music and Nature</em> 1: 99–132.</p>
Cleaned data, cleaning code and analysis code for 'Feedback timing affects L2+ perceptual vowel acquisition'
<p>This dataset uses 4.3.1 and the analysis code requires use of the groundhog package (Simonsohn & Gruson, 2021) to aid reproducibility.</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.