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        <namePart type="given">Mika</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.692</identifier>
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%0 Conference Proceedings
%T Finnish Dialect Identification: The Effect of Audio and Text
%A Hämäläinen, Mika
%A Alnajjar, Khalid
%A Partanen, Niko
%A Rueter, Jack
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F hamalainen-etal-2021-finnish
%X Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.
%R 10.18653/v1/2021.emnlp-main.692
%U https://aclanthology.org/2021.emnlp-main.692/
%U https://doi.org/10.18653/v1/2021.emnlp-main.692
%P 8777-8783

Markdown (Informal)

[Finnish Dialect Identification: The Effect of Audio and Text](https://aclanthology.org/2021.emnlp-main.692/) (Hämäläinen et al., EMNLP 2021)

• Finnish Dialect Identification: The Effect of Audio and Text (Hämäläinen et al., EMNLP 2021)

ACL

• Mika Hämäläinen, Khalid Alnajjar, Niko Partanen, and Jack Rueter. 2021. Finnish Dialect Identification: The Effect of Audio and Text. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8777–8783, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.