1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29062
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPlüss, Michel-
dc.contributor.authorDeriu, Jan Milan-
dc.contributor.authorSchraner, Yanick-
dc.contributor.authorPaonessa, Claudio-
dc.contributor.authorHartmann, Julia-
dc.contributor.authorSchmidt, Larissa-
dc.contributor.authorScheller, Christian-
dc.contributor.authorHürlimann, Manuela-
dc.contributor.authorSamardžic, Tanja-
dc.contributor.authorVogel, Manfred-
dc.contributor.authorCieliebak, Mark-
dc.date.accessioned2023-11-10T18:37:30Z-
dc.date.available2023-11-10T18:37:30Z-
dc.date.issued2023-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29062-
dc.description.abstractWe present STT4SG-350, a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computational Linguisticsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSpeech corpusde_CH
dc.subjectSpeech-to-textde_CH
dc.subjectSwiss Germande_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.subject.ddc430: Deutschde_CH
dc.titleSTT4SG-350 : a speech corpus for all Swiss German dialect regionsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.18653/v1/2023.acl-short.150de_CH
dc.identifier.doi10.21256/zhaw-29062-
zhaw.conference.details61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 9-14 July 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1772de_CH
zhaw.pages.start1763de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)de_CH
zhaw.funding.snf200729de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.funding.zhawEnd-to-End Low-Resource Speech Translation for Swiss German Dialectsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2023_Pluess-etal_STT4SG-350-speech-corpus-Swiss-German-dialect.pdf239.59 kBAdobe PDFThumbnail
View/Open
Show simple item record
Plüss, M., Deriu, J. M., Schraner, Y., Paonessa, C., Hartmann, J., Schmidt, L., Scheller, C., Hürlimann, M., Samardžic, T., Vogel, M., & Cieliebak, M. (2023). STT4SG-350 : a speech corpus for all Swiss German dialect regions [Conference paper]. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1763–1772. https://doi.org/10.18653/v1/2023.acl-short.150
Plüss, M. et al. (2023) ‘STT4SG-350 : a speech corpus for all Swiss German dialect regions’, in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, pp. 1763–1772. Available at: https://doi.org/10.18653/v1/2023.acl-short.150.
M. Plüss et al., “STT4SG-350 : a speech corpus for all Swiss German dialect regions,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023, pp. 1763–1772. doi: 10.18653/v1/2023.acl-short.150.
PLÜSS, Michel, Jan Milan DERIU, Yanick SCHRANER, Claudio PAONESSA, Julia HARTMANN, Larissa SCHMIDT, Christian SCHELLER, Manuela HÜRLIMANN, Tanja SAMARDŽIC, Manfred VOGEL und Mark CIELIEBAK, 2023. STT4SG-350 : a speech corpus for all Swiss German dialect regions. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Conference paper. Association for Computational Linguistics. 2023. S. 1763–1772
Plüss, Michel, Jan Milan Deriu, Yanick Schraner, Claudio Paonessa, Julia Hartmann, Larissa Schmidt, Christian Scheller, et al. 2023. “STT4SG-350 : A Speech Corpus for All Swiss German Dialect Regions.” Conference paper. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1763–72. Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-short.150.
Plüss, Michel, et al. “STT4SG-350 : A Speech Corpus for All Swiss German Dialect Regions.” Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics, 2023, pp. 1763–72, https://doi.org/10.18653/v1/2023.acl-short.150.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.