{"data":{"id":"10.6084/m9.figshare.7628819","type":"dois","attributes":{"doi":"10.6084/m9.figshare.7628819","prefix":"10.6084","suffix":"m9.figshare.7628819","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Hlavnička, Jan","nameType":"Personal","givenName":"Jan","familyName":"Hlavnička","affiliation":[],"nameIdentifiers":[]},{"name":"Čmejla, Roman","nameType":"Personal","givenName":"Roman","familyName":"Čmejla","affiliation":[],"nameIdentifiers":[]},{"name":"Jiří Klempíř","affiliation":[],"nameIdentifiers":[]},{"name":"Evžen Růžička","affiliation":[],"nameIdentifiers":[]},{"name":"Rusz, Jan","nameType":"Personal","givenName":"Jan","familyName":"Rusz","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Synthetic vowels of speakers with Parkinson’s disease and Parkinsonism"}],"publisher":"figshare","container":{},"publicationYear":2019,"subjects":[{"subject":"91301 Acoustics and Noise Control (excl. Architectural Acoustics)","subjectScheme":"FOR"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mechanical engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"90399 Biomedical Engineering not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Medical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Medical engineering","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"200404 Laboratory Phonetics and Speech Science","subjectScheme":"FOR"},{"subject":"FOS: Languages and literature","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Languages and literature","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-01-25","dateType":"Created"},{"date":"2019-10-29","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["933761598 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The dataset contains synthesized replicas of sustained vowels /A/ and /I/ performed by healthy controls, patients with Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. The dataset can be used as a reference for evaluation of pitch detectors, detectors of modal fundamental frequency, and detectors of subharmonics.\u003cbr\u003e\u003cb\u003eCoding system \u003c/b\u003eEach recording is named by a unique alphanumeric code in the format \u003ci\u003eUvxy\u003c/i\u003e, where \u003ci\u003eU\u003c/i\u003e means abbreviation of the group (HC = healthy control, PD = Parkinson’s disease, MSA = multiple system atrophy, PSP = progressive supranuclear palsy) in upper case characters, \u003ci\u003ev\u003c/i\u003e is the numeric identifier of the subject within the group, \u003ci\u003ex\u003c/i\u003e denotes type of vowel (a = vowel /A/, i = vowel /I/), and \u003ci\u003ey\u003c/i\u003e is the number of repetition. The part of the code of \u003ci\u003eU\u003c/i\u003e and \u003ci\u003ev\u003c/i\u003e uniquely determine each speaker, whereas \u003ci\u003ex\u003c/i\u003e and \u003ci\u003ey\u003c/i\u003e determine speaker’s recordings.\u003cbr\u003e\u003cb\u003e Recordings \u003c/b\u003eAll recordings are briefly described in table dataset.csv. All files of each record (see records.zip) are identified by the corresponding code and suffix. Suffix describe type of the file and is separated from the code by underscore. Naming of the files is illustrated on the record HC8a1. The code describes first repetition of the vowel /A/ performed by healthy speaker HC8. The record HC8a1 consists of following files:\u003ci\u003eHC8a1.wav\u003c/i\u003e = waveform of the synthesized replica. This is the reference signal used for the evaluation. Parameters of jitter, shimmer and harmonic to noise ratio (HNR) can be found in dataset.csv.\u003cbr\u003e\u003ci\u003eHC8a1_clean.wav\u003c/i\u003e = waveform of the synthesized replica without added noise. We provide this signal to make the model more versatile. Authors may add a different kind of noise to this signal or manipulate with HNR. Note that that both signals required normalization prior to writing into wav-file. Original scaling factor between HC8a1 and HCa1_clean can be determined from total power of signals and reference HNR value.\u003cbr\u003e\u003ci\u003eHC8a1_LF.wav\u003c/i\u003e = sample of the glottal pulse used for the synthesis.\u003cbr\u003e\u003ci\u003eHC8a2_impulses.csv\u003c/i\u003e = list of impulses’ locations in seconds and corresponding amplitudes. The position of pulses was corrected to match with the beginning of glottal pulse, i.e., first sample of the signal HC8a1_LF.wav begins at each of these positions. The jitter and shimmer listed in dataset.csv were are median values. Jitter and shimmer by other definitions can be calculated from positions and amplitudes of pulses provided by this file. \u003ci\u003eHC8a1_subharmonics.csv\u003c/i\u003e = list of subharmonic intervals described by the start time in seconds and end time in seconds. Corresponding index of amplitude modulation expressed as SHR in percent can be found in the table dataset.csv. When no subharmonic was determined by the supervised parameterization, no file was included for the speaker and SHR in the dataset.csv was set to zero [1].\u003cbr\u003e \u003cbr\u003e [1] Note that the supervised detection had lower sensitivity due to senzitivity of pitch trace in PRAAT, so the occurrence of subharmonics in synthesized data is much lower than in the original dataset analyzed by automated segmentation. This is not a problem because subharmonics were synthesized only at the given interval - this illustrates why it is important to detect subharmonics in other way than by pitch. 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