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.
16
datasets available to search
ShareScore release 0.9.0
Dataset results
16 results for “syllable”
Syllable level speech sequencing
Open the record for dataset details and reuse information.
Jingju a cappella singing syllable boundary and duration annotation dataset
<p>This dataset is a collection of syllable boundary annotations and syllable duration annotations of a cappella singing performed by jingju (京剧, Beijing opera) professional and amateur singers. This dataset was used as the experimental dataset in the following work:</p> <blockquote> <p>Rong Gong, Nicolas Obin, Georgi Dzhambazov and Xavier Serra, “Score-Informed syllable segmentation for jingju a cappella singing voice with Mel-frequency intensity profiles," in<em> Folk Music Analysis workshop (FMA) 2017, Málaga, Spain</em></p> </blockquote> <p><strong>Audio Content</strong></p> <p>The audio files are the a cappella singing arias recordings, which are stereo or mono, sampled at 44.1 kHz, and stored as wav files. They can be found at this link http://doi.org/10.5281/zenodo.344932</p> <p>The wav files are recorded by two institutes: those file names ending with ‘qm’ are recorded by C4DM Queen Mary University of London; others file names ending with ‘upf’ or ‘lon’ are recorded by MTG-UPF. If you use the dataset in your work, please cite the following publication.</p> <blockquote> <p>D. A. A. Black, M. Li, and M. Tian, “Automatic Identification of Emotional Cues in Chinese Opera Singing,” in <em>13th Int. Conf. on Music </em><em>Perception and Cognition</em> (ICMPC-2014), 2014, pp. 250–255.</p> </blockquote> <p><strong>Annotations</strong></p> <p>The syllable boundary annotation is in Textgrid format (Praat). The annotation is done in both phrase-level and syllable-level. The syllable duration annotation is in cvs format. Please consult Readme text in both folders for further details. The parsing code of the annotation files is provided in ‘pycode’ folder. </p> <p><strong>Availability of the Dataset</strong></p> <p>The annotations and codes in this dataset are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Rong Gong: rong<dot>gong<at>upf<dot>edu</p> <p>Rafael Caro Repetto: rafael<dot>caro<at>upf<dot>edu</p>
Han-solo: Thai syllable segmenter
<p>This dataset is a Thai syllable corpus for the Thai social media domain from <a href="https://doi.org/10.5281/zenodo.3457447">Wisesight Sentiment Corpus</a>.</p> <ul> <li>Train: 794 lines</li> <li>Test: 199 lines</li> <li>Total: 993 lines</li> </ul> <p>This dataset is a part of the <a href="https://github.com/PyThaiNLP">PyThaiNLP project</a>.</p>
Clear Speech Data for Syllable-Rate-Adjusted-Modulation (SRAM)
<p>This Dataset is associated with the paper "Syllable-Rate-Adjusted-Modulation (SRAM) Predicts Clear and Conversational Speech Intelligibility". It contains 144 sentences recorded from two talkers (one female, one male) in both clear and conversational styles (72 sentences in each style). The sample rate was 16000Hz. The silence periods before and after the speech were removed. The speech scripts for each speech style and the human performance are included in each sub-folder.<br> SSN.wav is the steady-state noise used to create the noisy speeches.</p> <p>File Structure:<br> - Female<br> - Clear<br> - 1.wav<br> - ...<br> - 72.wav<br> - Convo<br> - 1.wav<br> - ...<br> - 72.wav<br> - human_results.csv<br> - key_words_clear.txt<br> - key_words_conv.txt<br> - Male<br> - SSN.wav</p> <p> </p>
Ukrainian 14-syllable verse in Belarusian poetry: the rhythm of translations and imitations (dataset)
<p>Data and source code accompanying the talk:<br> У. В. Парыцкі. Украінскі 14-складовы верш у беларускай паэзіі: рытміка перакладаў і імітацый // X Міжнародны Кангрэс даследчыкаў Беларусі, Коўна, 01.10.2022 [Vladislav Poritski. Ukrainian 14-syllable verse in Belarusian poetry: the rhythm of translations and imitations // Presented at 10th International Congress of Belarusian Studies, Kaunas, 01.10.2022]</p> <p>The empirical investigation of 14-syllable verse, presented in the talk, is based upon a sample of Ukrainian texts by Taras Shevchenko, their Belarusian translations, and original Belarusian poetry by Yanka Kupala, Yakub Kolas, Piatruś Brouka. The dataset structure is as follows:</p> <ul> <li>./0_plain – plain texts;</li> <li>./1_accentuated – accentuated texts;</li> <li>metadata_shevchenko.tsv, metadata_be_authors.tsv – metadata files describing the texts;</li> <li>make_reports.py – a Python script to generate statistic reports from the accentuated texts;</li> <li>./2_reports – programmatically generated reports;</li> <li>slides.tex – LaTeX source code of the talk's slides, where the reports are embedded as diagrams and tables;</li> <li>slides.pdf – PDF version of the slides.</li> </ul> <p>The directories ./0_plain, ./1_accentuated, ./2_reports are provided in .zip archives.</p> <p>Belarusian translations of Taras Shevchenko's poetry have been taken from the book:<br> Т. Р. Шаўчэнка. Вершы. Паэмы. Мінск: Мастацкая літаратура, 1989.<br> (scan copy available at https://files.knihi.com/Knihi/scanned/Saucenka.Viersy_paemy.djvu)<br> Each poem is stored in a separate .txt file. The file name indicates the number of the poem's first page in the scanned book, e.g.: 021.txt. Same names are used for the respective Ukrainian texts. In each pair of files, such as e.g. ./0_plain/uk/021.txt and ./0_plain/be/021.txt, the texts are aligned line by line. Poem titles in both languages, translator names, and the URLs of Ukrainian source texts are provided in metadata_shevchenko.tsv.</p> <p>Original Belarusian poetry, kept in ./0_plain/be, doesn't require any alignment, and the naming scheme is different. Poem titles, author names, and the URLs of Belarusian source texts are provided in metadata_be_authors.tsv.</p> <p>In all Ukrainian and Belarusian texts, metrically irrelevant lines are discarded, only 14-syllable verse lines are stored, each of them split graphically into 8+6 syllables. Occasional minor violations, i.e. ± one or two syllables, are allowed in the texts but ignored in the statistic reports. No spans shorter than a pair of rhyming 14-syllable lines (or, graphically, a quatraine of 8+6+8+6 syllables) were sampled from polymetric poems.</p> <p>These special characters are used:</p> <ul> <li>"/" to represent line break in the source edition;</li> <li>"//" for section break (next stanza, another character's words);</li> <li>trailing "#" for the inverse of line break: to recover the original 14-syllable line as printed in the source edition, one should remove the newline;</li> <li>leading "#" for mis-aligned lines, e.g. those missing in the Belarusian translation and added hypothetically, in order to restore the alignment.</li> </ul> <p>The procedure of accentuating Ukrainian and Belarusian texts was semi-automatic, using an opportunistic database of word accents crawled from online lexicographic resources: https://slounik.org for Belarusian, https://uk.wiktionary.org and https://slovnyk.ua/nagolos.php for Ukrainian. The database and the accentuator script are not part of this dataset. Although a fair bit of manual supervision was put into ensuring that most accents are accurate, it's likely that some errors still remain, especially in the Ukrainian data, so please be cautious.</p> <p>Accentuated texts in ./1_accentuated/uk and ./1_accentuated/be are lowercased, with all punctuation stripped off. As usual in quantitative study of East Slavic verse (see e.g. https://doi.org/10.12697/smp.2019.6.2.02 for a recent overview), we distinguish between two kinds of stresses: pronouns and certain other function words bear "light" stress, while content words bear "heavy" stress. These are the designations:</p> <ul> <li>"`" for light stress, to the left of the stressed vowel;</li> <li>"'" for heavy stress, to the right of the stressed vowel (note that after consonants, "'" is an apostrophe);</li> <li>"*" for variant heavy stress, as in Ukrainian <em>ба*йду*же</em>;</li> <li>"_" to group clitics together with stressed words, as in Ukrainian <em>і_не_привіта'ла</em>.</li> </ul> <p>In rare exceptional cases, the meter may require to pronounce syllabic consonants, as in Belarusian <em>рэестр</em>. To match pronunciation, we add a vowel in square brackets: <em>рэест[а]р</em>.</p> <p>The reports summarize certain statistic properties of the dataset:</p> <ul> <li>translators.csv – a breakdown of Shevchenko's Belarusian translations into the numbers of lines contributed by each translator. 8+6 are counted as separate lines. Syllable count violations are ignored: a pair of aligned Ukrainian / Belarusian lines is not counted towards the translator's total, if the number of syllables is irrelevant (e.g. 9 and 9) or mismatched (e.g. 8 and 6).</li> <li>be_authors.csv – line counts by author in the original Belarusian poetry. Same counting rules apply, modulo the alignment.</li> <li>rhythm.csv – percentages of accents on each of the 14 syllables in various samples, grouped by author and / or translator. Rows are syllables, columns are samples. Accents in each sample are counted two ways: "min" – only heavy stresses, "max" – all stresses.</li> <li>total_accentuation.csv – average accent counts per line in Shevchenko's Ukrainian texts and Belarusian translations, separately for 8+6, separately for heavy and all stresses.</li> <li>word_boundary.csv – statistics of word boundary positions in 8-syllable 2-word heavy-stressed lines in Shevchenko's Ukrainian texts and Belarusian translations.</li> <li>trochaicity.csv – ratio of stresses that match trochaic metrical template, separately for 8+6, heavy stresses only. Rows are samples: Shevchenko's Ukrainian texts and Belarusian translations, original poetry by three Belarusian authors.</li> </ul> <p>For implementation details, see the source code of make_reports.py.</p> <p>To reproduce report generation, you will need Python. Unzip the archive 1_accentuated.zip and run:<br> python3 make_reports.py</p> <p>To rebuild the slides, you will need LaTeX:<br> xelatex -synctex=1 -interaction=nonstopmode -shell-escape slides.tex<br> If the bibliographic references are not rendered properly, rerun once again.</p>
Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
<p>This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>.</p> <p>The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links:</p> <p>jingju dataset part1: <a href="https://zenodo.org/record/1185154">https://zenodo.org/record/1185154</a></p> <p>jingju dataset part2: <a href="https://doi.org/10.5281/zenodo.842229">https://doi.org/10.5281/zenodo.842229</a></p> <p>Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>.</p> <p>Set <em>path_jingju_dataset</em> to the parent path of these three datasets.</p> <p>Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset).</p> <p>Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1.</p> <p>Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2.</p> <p>For more information on this paper, please refer to the Github page: <a href="https://github.com/ronggong/interspeech2018_submission01">https://github.com/ronggong/interspeech2018_submission01</a></p> <p> </p>
Acoustic feature measurements of male and female NZ bellbird (Anthornis melanura) song syllables
<p>Acoustic feature measurements of 20,700 syllables (acoustic units) of male and female birdsong, from NZ bellbirds (Anthornis melanura). The measurements were extracted in Koe bioacoustics software (koe.io.ac.nz), on recordings from six sites in the Hauraki Gulf, northeastern New Zealand. The sites are Tawhiti Rahi island (Poor Knights island group), Lady Alice Island (Hen and Chicks island group), Hauturu (Little Barrier Island), Tawharanui Peninsula, Repanga (Cuvier Island), and Tiritiri Matangi Island.</p> <p>Descriptions of extracted acoustic features can be found at https://github.com/fzyukio/koe/wiki#extract-unit-features</p>
New Zealand Bellbird (Anthornis melanura) syllable database from Tiritiri Matangi Island, New Zealand
<p>Database of syllables for New Zealand Bellbirds (<em>Anthornis melanura</em>) recorded on Tiritiri Matangi Island, Auckland, New Zealand. Recordings were taken from 2012 to 2015. Database extracted from bioacoustics software Koe (www.koe.io.ac.nz) on 09 October 2018. Each syllable from song selections have syllable types and syllable family types labelled. Recording season refers to the southern hemisphere breeding season and post-breeding season months of August to May for each consecutive season. Data used by the following study published on bioRxiv <a href="https://doi.org/10.1101/2021.09.29.462458">https://doi.org/10.1101/2021.09.29.462458</a></p> <p><strong>Column key:</strong></p> <p>id = unique identifier for every syllable</p> <p>duration = syllable duration (seconds)</p> <p>song_individual = Unique identifier for each individual bird recorded</p> <p>sex = sex of the individual</p> <p>start_time = start time of the syllable within song selection (seconds)</p> <p>label_family = syllable family</p> <p>end_time = end time of the syllable within song selection (seconds)</p> <p>label = syllable type</p> <p>song_track = name of song selection</p> <p>song_date = Date song recorded (day-month-year)</p> <p>Year = Year song recorded</p> <p>Month = Month song recorded</p> <p>Day = Day song recorded</p> <p>rec-season = Recording season</p>
Audio-tactile syllables EEG Dataset
<p>Dataset setting out to investigate behavioural and EEG responses to syllables coupled with tactile pulses at a 5Hz rhythm. The raw behavioural and EEG data is provided here.</p> <p><strong>#Introduction</strong></p> <p>This dataset contains the behavioural response to one syllable discrimination task in which subjects were subjected to regular tactile stimuli followed by a syllable in noise. It also contains the EEG response recorded during the task. There were 16 subjects, which IDs are : 'camembert', 'leader', 'bristol', 'policeman', 'frenchship', 'preacher', 'pretzel2new', 'canada', 'rotiqueen', 'kitten', 'batman', '2d', 'laundry2', 'bearcub', 'stopwatch' and 'tala'.</p> <p><strong>#Content</strong></p> <p>The dataset contains a folder named after each subject: these contain the eeg recordings for each of them as well as behavioural data. </p> <ul> <li><strong>id_Entrainment.csv: </strong>This csv file contains all the behvioural data about the subject (id). The different fields are as follow: <ul> <li>Trial: The ID of the current trial</li> <li>Type: Whether the tactile stimuli was audio-tactile rhythmic, audio-tactile random or audio-only</li> <li>Phase: In case it is audio-tactile, what is the phase between the onset of the vowel and the tactile rhythm. Note that we refer here to the phase relative to the rhythm of the tactile stimuli and not a measured rhythm. This choice was made as to sample the delays between audio and tactile as to be between 1 and 2 periods away from the rhythm we would expect to emerge from brain activity. The four phases can easily be translated to the following audio-tactile delays between the last tactile pulse and the onset of the syllable as: 150ms, 200ms, 250ms, 300ms.</li> <li>Score: 1 if the right syllable was selected by the subject, 0 otherwise.</li> <li>Syllable: the presented syllable out of the following set: ka, ga, ba, pa, da, ta</li> <li>Gender: The gender of the speaking voice, m for male, f for female</li> <li>Shift: A random shift between the onset of the trial and the first tactile pulse. Note that this value is representing the number of samples at a sampling frequency of 39062.5 (imposed by our hardware: TDT RX8)</li> <li>Random: The number of the random tactile stimulation. Since these were generated procedurally, we recorded them for reference, however, they do not play a part in the analysis. </li> </ul> </li> <li><strong>id.eeg, id.vhdr, id.vmrk:</strong> These correspond to the recorded data. More information on these file formats can be found on the <a href="https://www.fieldtriptoolbox.org/getting_started/brainvision/">Brian vision webpage</a>. </li> </ul> <p><strong>#EEG Data Format</strong></p> <p>We recorded continuous EEG from the participants over an hour at 1kHz. </p> <p>In addition to the 63 channels, there is also Stimtrack channel labelled as 'Sound' which was tracking an addition of the auditory and tactile stimuli and allowed to track the sent stimuli as well as the alignment. The corresponding files can be found using the provided .csv file.</p> <p> The .vmrk file provided contains the timing of the triggers sent at the start of each trial and can help with tracking the progress in the experiment. </p>
Data from: The relative roles of cultural drift and acoustic adaptation in shaping syllable repertoires of island bird populations change with time since colonization
In birds, song divergence often precedes and facilitates divergence of other traits. We assessed the relative roles of cultural drift, innovation and acoustic adaptation in divergence of island bird dialects, using silvereyes (Zosterops lateralis). In recently colonized populations, syllable diversity was not significantly lower than source populations, shared syllables between populations decreased with increasing number of founder events and dialect variation displayed contributions from both habitat features and drift. The breadth of multivariate space occupied by recently colonized Z. l. lateralis populations was comparable to evolutionarily old forms that have diverged over thousands to hundreds of thousands of years. In evolutionarily old subspecies, syllable diversity was comparable to the mainland and the amount of variation in syllable composition explained by habitat features increased by two- to three-fold compared to recently colonized populations. Together these results suggest that cultural drift influences syllable repertoires in recently colonized populations, but innovation likely counters syllable loss from colonization. In evolutionarily older populations, the influence of acoustic adaptation increases, possibly favoring a high diversity of syllables. These results suggest that the relative importance of cultural drift and acoustic adaptation changes with time since colonization in island bird populations, highlighting the value of considering multiple mechanisms and timescale of divergence when investigating island song divergence.
Syllable Structure and Morphemicity in Tone Patterns on Verbs in Kanise Khumi
<p>In the Khomic group within Kuki-Chin, there have been conflicting representations of whether all morphemes bear tone, or whether tonelessness is related to syllable type. While verb roots in these languages can be monosyllabic or sesquisyllabic, they are always bound by affixes and clitics. This study offers a fine-grained analysis of tonal variation on various syllable types and morphemes. I examine verbs in Kanise Khumi using archived wordlist data. In addition to pitch specification in various contexts, I focus on the durability of voice quality cues that are associated with Kanise tones.</p>
Preliminary Acoustic Analysis of Minor Syllables in Kanise Khumi
<p>In the Kanise Khumi language [Myanmar; Kuki- Chin; Khomic branch], purported minor syllables appear to exhibit surface variations in segmental features. However, it is unexpected for minor syllables in Khomic languages to show contrastive variations. This talk presents the first acoustic analysis of Kanise Khumi's minor syllables using an articulatory framework to discover such possibility and ends with a discussion of the implication of the study towards the establishment of minor syllable and thence sesquisyllable in the language.</p>
Supporting data (pre-processed) for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition"
<p>Pre-processed data. </p> <p>Data were re-referenced off-line to the average of left and right mastoid electrodes, bandpass filtered from 5 to 100 Hz (4th order Butterworth filter) and then segmented to include 200 ms before and 2000 ms after stimulus onset. Epoched data were normalized based on a prestimulus period of 200 ms, and then evaluated according to a sample-by-sample procedure to remove noisy sensors that were replaced using spherical splines. Additionally, EEG epochs that contained data samples exceeding threshold (100 uV) were excluded on a sensor-by-sensor basis, including horizontal and vertical eye channels</p> <p>The original data:<br> Montani, Veronica. (2019). Supporting data for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3260451</p>
Data from: The relative roles of cultural drift and acoustic adaptation in shaping syllable repertoires of island bird populations change with time since colonization
Open the record for dataset details and reuse information.
PROVIDING KNOWLEDGE ABOUT SYLLABLES IN THE MOTHER TONGUE TEXTBOOK FOR CLASS 2
Open the record for dataset details and reuse information.
Supporting data for: "Frequency-tagged visual evoked responses track syllable effects in visual word recognition"
<p>The dataset consists of the original 17 .bdf files.</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.