{"data":{"id":"10.17026/pt/2aamns","type":"dois","attributes":{"doi":"10.17026/pt/2aamns","prefix":"10.17026","suffix":"pt/2aamns","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"D. Bikker","nameType":"Personal","affiliation":["Netherlands Forensic Institute"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0007-2665-1951","nameIdentifierScheme":"ORCID"}]},{"name":"E. Sergidou","nameType":"Personal","affiliation":["Netherlands Forensic Institute"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7280-3249","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"SCC"}],"publisher":"DANS Data Station Physical and Technical Sciences","container":{},"publicationYear":2026,"subjects":[{"subject":"Computer and Information Science"},{"subject":"automatic speaker verification"},{"subject":"signal processing"},{"subject":"speech processing"}],"contributors":[{"name":"Bikker, David","nameType":"Personal","givenName":"David","familyName":"Bikker","contributorType":"ContactPerson","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2025-09-24","dateType":"Submitted"},{"date":"2026-10-29","dateType":"Available"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"wave files","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["31536281600"],"formats":["application/octet-stream"],"version":"1.0","rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"lang":"en","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":"\u0026lt;p\u0026gt;A synthesized dataset consisting of 135,000 wave files in two separate sets of train, dev and test splits. Each wave file has a certain voice uttering a certain sentence, with certain channel properties (noise, reverb, and filtering). For both sets of splits, there are 100 different speakers, 100 different sentences, and 100 different channel settings. The dataset can be used for signal processing classification tasks of speaker, content and channel attributes. It was constructed for research into automatic speaker verification, see https://github.com/NetherlandsForensicInstitute/interpretability_in_speaker_verification. The code for this project can be used to generate a dataset like this one (depending on random seeds and computer architecture, it might be exactly the same or have different examples). The dataset as provided here can be used independently of the project.\u0026lt;/p\u0026gt;\u0026lt;p\u0026gt;\nThe archive is set up as follows:\u0026lt;br\u0026gt;\nIn the root are 18 files with the provided splits, consisting of lines of a class label and a filepath. There are also 2 files which show which files from VOiCES and LJ Speech were used in synthesis.\u0026lt;/p\u0026gt;\u0026lt;p\u0026gt;\nThe 'wav' folder has the main wave files of the dataset, in subdirectories for each speaker.\nThe 'unaugmented' folder contains the synthesized utterances before channel augmentation was done.\u0026lt;/p\u0026gt;\u0026lt;p\u0026gt;\nThe 'speaker-wav' folder contains the files from VOiCES that the speakers were based on.\nNeither 'unaugmented' nor 'speaker-wav' are necessary to use the dataset, but they are included to allow more insight into how the dataset was created, as well as to allow redoing part of the generation (for example by using different channel settings).\u0026lt;/p\u0026gt;","descriptionType":"Abstract"},{"description":"xTTS, 2","descriptionType":"TechnicalInfo"},{"description":"SciPy, 1.15.1","descriptionType":"TechnicalInfo"},{"description":"NumPy, 2.1.3","descriptionType":"TechnicalInfo"},{"description":"This dataset uses four existing datasets. VOiCES and MUSAN were released under a CC BY 4.0 license, RIR was released under an Apache 2.0 license, and LJ Speech was released into the public domain.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHJlc291cmNlIHhtbG5zPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCIgeG1sbnM6eHNpPSJodHRwOi8vd3d3LnczLm9yZy8yMDAxL1hNTFNjaGVtYS1pbnN0YW5jZSIgeHNpOnNjaGVtYUxvY2F0aW9uPSJodHRwOi8vZGF0YWNpdGUub3JnL3NjaGVtYS9rZXJuZWwtNCBodHRwOi8vc2NoZW1hLmRhdGFjaXRlLm9yZy9tZXRhL2tlcm5lbC00LjUvbWV0YWRhdGEueHNkIj4KICA8aWRlbnRpZmllciBpZGVudGlmaWVyVHlwZT0iRE9JIj4xMC4xNzAyNi9QVC8yQUFNTlM8L2lkZW50aWZpZXI+CiAgPGNyZWF0b3JzPgogICAgPGNyZWF0b3I+CiAgICAgIDxjcmVhdG9yTmFtZSBuYW1lVHlwZT0iUGVyc29uYWwiPkQuIEJpa2tlcjwvY3JlYXRvck5hbWU+CiAgICAgIDxuYW1lSWRlbnRpZmllciBuYW1lSWRlbnRpZmllclNjaGVtZT0iT1JDSUQiIHNjaGVtZVVSST0iaHR0cHM6Ly9vcmNpZC5vcmciPmh0dHBzOi8vb3JjaWQub3JnLzAwMDktMDAwNy0yNjY1LTE5NTE8L25hbWVJZGVudGlmaWVyPgogICAgICA8YWZmaWxpYXRpb24gYWZmaWxpYXRpb25JZGVudGlmaWVyPSJodHRwczovL3Jvci5vcmcvMDRzMno0MjkxIiBzY2hlbWVVUkk9Imh0dHBzOi8vcm9yLm9yZyIgYWZmaWxpYXRpb25JZGVudGlmaWVyU2NoZW1lPSJST1IiPk5ldGhlcmxhbmRzIEZvcmVuc2ljIEluc3RpdHV0ZTwvYWZmaWxpYXRpb24+CiAgICA8L2NyZWF0b3I+CiAgICA8Y3JlYXRvcj4KICAgICAgPGNyZWF0b3JOYW1lIG5hbWVUeXBlPSJQZXJzb25hbCI+RS4gU2VyZ2lkb3U8L2NyZWF0b3JOYW1lPgogICAgICA8bmFtZUlkZW50aWZpZXIgbmFtZUlkZW50aWZpZXJTY2hlbWU9Ik9SQ0lEIiBzY2hlbWVVUkk9Imh0dHBzOi8vb3JjaWQub3JnIj5odHRwczovL29yY2lkLm9yZy8wMDAwLTAwMDEtNzI4MC0zMjQ5PC9uYW1lSWRlbnRpZmllcj4KICAgICAgPGFmZmlsaWF0aW9uIGFmZmlsaWF0aW9uSWRlbnRpZmllcj0iaHR0cHM6Ly9yb3Iub3JnLzA0czJ6NDI5MSIgc2NoZW1lVVJJPSJodHRwczovL3Jvci5vcmciIGFmZmlsaWF0aW9uSWRlbnRpZmllclNjaGVtZT0iUk9SIj5OZXRoZXJsYW5kcyBGb3JlbnNpYyBJbnN0aXR1dGU8L2FmZmlsaWF0aW9uPgogICAgPC9jcmVhdG9yPgogIDwvY3JlYXRvcnM+CiAgPHRpdGxlcz4KICAgIDx0aXRsZT5TQ0M8L3RpdGxlPgogIDwvdGl0bGVzPgogIDxwdWJsaXNoZXI+REFOUyBEYXRhIFN0YXRpb24gUGh5c2ljYWwgYW5kIFRlY2huaWNhbCBTY2llbmNlczwvcHVibGlzaGVyPgogIDxwdWJsaWNhdGlvblllYXI+MjAyNjwvcHVibGljYXRpb25ZZWFyPgogIDxzdWJqZWN0cz4KICAgIDxzdWJqZWN0PkNvbXB1dGVyIGFuZCBJbmZvcm1hdGlvbiBTY2llbmNlPC9zdWJqZWN0PgogICAgPHN1YmplY3Q+YXV0b21hdGljIHNwZWFrZXIgdmVyaWZpY2F0aW9uPC9zdWJqZWN0PgogICAgPHN1YmplY3Q+c2lnbmFsIHByb2Nlc3Npbmc8L3N1YmplY3Q+CiAgICA8c3ViamVjdD5zcGVlY2ggcHJvY2Vzc2luZzwvc3ViamVjdD4KICA8L3N1YmplY3RzPgogIDxjb250cmlidXRvcnM+CiAgICA8Y29udHJpYnV0b3IgY29udHJpYnV0b3JUeXBlPSJDb250YWN0UGVyc29uIj4KICAgICAgPGNvbnRyaWJ1dG9yTmFtZSBuYW1lVHlwZT0iUGVyc29uYWwiPkJpa2tlciwgRGF2aWQ8L2NvbnRyaWJ1dG9yTmFtZT4KICAgICAgPGdpdmVuTmFtZT5EYXZpZDwvZ2l2ZW5OYW1lPgogICAgICA8ZmFtaWx5TmFtZT5CaWtrZXI8L2ZhbWlseU5hbWU+CiAgICA8L2NvbnRyaWJ1dG9yPgogIDwvY29udHJpYnV0b3JzPgogIDxkYXRlcz4KICAgIDxkYXRlIGRhdGVUeXBlPSJTdWJtaXR0ZWQiPjIwMjUtMDktMjQ8L2RhdGU+CiAgICA8ZGF0ZSBkYXRlVHlwZT0iQXZhaWxhYmxlIj4yMDI2LTEwLTI5PC9kYXRlPgogIDwvZGF0ZXM+CiAgPHJlc291cmNlVHlwZSByZXNvdXJjZVR5cGVHZW5lcmFsPSJEYXRhc2V0Ij53YXZlIGZpbGVzPC9yZXNvdXJjZVR5cGU+CiAgPHNpemVzPgogICAgPHNpemU+MzE1MzYyODE2MDA8L3NpemU+CiAgPC9zaXplcz4KICA8Zm9ybWF0cz4KICAgIDxmb3JtYXQ+YXBwbGljYXRpb24vb2N0ZXQtc3RyZWFtPC9mb3JtYXQ+CiAgPC9mb3JtYXRzPgogIDx2ZXJzaW9uPjEuMDwvdmVyc2lvbj4KICA8cmlnaHRzTGlzdD4KICAgIDxyaWdodHMgcmlnaHRzVVJJPSJpbmZvOmV1LXJlcG8vc2VtYW50aWNzL29wZW5BY2Nlc3MiLz4KICAgIDxyaWdodHMgcmlnaHRzVVJJPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS80LjAiIHhtbDpsYW5nPSJlbiI+Q3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbiA0LjAgSW50ZXJuYXRpb25hbCBMaWNlbnNlLjwvcmlnaHRzPgogIDwvcmlnaHRzTGlzdD4KICA8ZGVzY3JpcHRpb25zPgogICAgPGRlc2NyaXB0aW9uIGRlc2NyaXB0aW9uVHlwZT0iQWJzdHJhY3QiPiZhbXA7bHQ7cCZhbXA7Z3Q7QSBzeW50aGVzaXplZCBkYXRhc2V0IGNvbnNpc3Rpbmcgb2YgMTM1LDAwMCB3YXZlIGZpbGVzIGluIHR3byBzZXBhcmF0ZSBzZXRzIG9mIHRyYWluLCBkZXYgYW5kIHRlc3Qgc3BsaXRzLiBFYWNoIHdhdmUgZmlsZSBoYXMgYSBjZXJ0YWluIHZvaWNlIHV0dGVyaW5nIGEgY2VydGFpbiBzZW50ZW5jZSwgd2l0aCBjZXJ0YWluIGNoYW5uZWwgcHJvcGVydGllcyAobm9pc2UsIHJldmVyYiwgYW5kIGZpbHRlcmluZykuIEZvciBib3RoIHNldHMgb2Ygc3BsaXRzLCB0aGVyZSBhcmUgMTAwIGRpZmZlcmVudCBzcGVha2VycywgMTAwIGRpZmZlcmVudCBzZW50ZW5jZXMsIGFuZCAxMDAgZGlmZmVyZW50IGNoYW5uZWwgc2V0dGluZ3MuIFRoZSBkYXRhc2V0IGNhbiBiZSB1c2VkIGZvciBzaWduYWwgcHJvY2Vzc2luZyBjbGFzc2lmaWNhdGlvbiB0YXNrcyBvZiBzcGVha2VyLCBjb250ZW50IGFuZCBjaGFubmVsIGF0dHJpYnV0ZXMuIEl0IHdhcyBjb25zdHJ1Y3RlZCBmb3IgcmVzZWFyY2ggaW50byBhdXRvbWF0aWMgc3BlYWtlciB2ZXJpZmljYXRpb24sIHNlZSBodHRwczovL2dpdGh1Yi5jb20vTmV0aGVybGFuZHNGb3JlbnNpY0luc3RpdHV0ZS9pbnRlcnByZXRhYmlsaXR5X2luX3NwZWFrZXJfdmVyaWZpY2F0aW9uLiBUaGUgY29kZSBmb3IgdGhpcyBwcm9qZWN0IGNhbiBiZSB1c2VkIHRvIGdlbmVyYXRlIGEgZGF0YXNldCBsaWtlIHRoaXMgb25lIChkZXBlbmRpbmcgb24gcmFuZG9tIHNlZWRzIGFuZCBjb21wdXRlciBhcmNoaXRlY3R1cmUsIGl0IG1pZ2h0IGJlIGV4YWN0bHkgdGhlIHNhbWUgb3IgaGF2ZSBkaWZmZXJlbnQgZXhhbXBsZXMpLiBUaGUgZGF0YXNldCBhcyBwcm92aWRlZCBoZXJlIGNhbiBiZSB1c2VkIGluZGVwZW5kZW50bHkgb2YgdGhlIHByb2plY3QuJmFtcDtsdDsvcCZhbXA7Z3Q7JmFtcDtsdDtwJmFtcDtndDsKVGhlIGFyY2hpdmUgaXMgc2V0IHVwIGFzIGZvbGxvd3M6JmFtcDtsdDticiZhbXA7Z3Q7CkluIHRoZSByb290IGFyZSAxOCBmaWxlcyB3aXRoIHRoZSBwcm92aWRlZCBzcGxpdHMsIGNvbnNpc3Rpbmcgb2YgbGluZXMgb2YgYSBjbGFzcyBsYWJlbCBhbmQgYSBmaWxlcGF0aC4gVGhlcmUgYXJlIGFsc28gMiBmaWxlcyB3aGljaCBzaG93IHdoaWNoIGZpbGVzIGZyb20gVk9pQ0VTIGFuZCBMSiBTcGVlY2ggd2VyZSB1c2VkIGluIHN5bnRoZXNpcy4mYW1wO2x0Oy9wJmFtcDtndDsmYW1wO2x0O3AmYW1wO2d0OwpUaGUgJmFtcDthcG9zO3dhdiZhbXA7YXBvczs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