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Towards a Dialect Classification in German Speech Samples

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Book cover Speech and Computer (SPECOM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11658))

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Abstract

The automatic classification of a speaker’s dialect can enrich many applications, e.g. in the human-machine interaction (HMI) or natural language processing (NLP) but also in specific areas such as pronunciation tutoring, forensic analysis or personalization of call-center talks. Although a lot of HMI/NLP-related research has been dedicated to different tasks in affective computing, emotion recognition, semantic understanding and other advanced topics, there seems to be a lack of methods for an automated dialect analysis that is not based on transcriptions, in particular for some languages like German. For other languages such as English, Mandarin or Arabic, a multitude of feature combinations and classification methods has been tried already, which provides a starting point for our study. We describe selected experiments to train suitable classifiers on German dialect varieties in the corpus “Regional Variants of German 1” (RVG1). Our article starts with a systematic choice of appropriate spectral features. In a second step, these features are post-processed with different methods and used to train one Gaussian Mixture Model (GMM) per feature combination as a Universal Background Model (UBM). The resulting UBMs are then adapted to a varied selection of dialects by maximum-a-posteriori (MAP) adaptation. Our preliminary results on German show, that a dialect discrimination and classification is possible. The unweighted recognition accuracy ranges from 32.4 to 54.9% in a 3-dialects test and from 19.6 to 31.4% in a classification of 9-dialects. Some dialects are easier distinguishable, purely using spectral features, while others require a different feature set or more sophisticated classification methods, which we will explore in future experiments.

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Authors and Affiliations

  1. Institute of Applied Informatics, Universität Leipzig, 04107, Leipzig, Germany

    Johanna Dobbriner

  2. Institute of Communications Engineering, Leipzig University of Telecommunications (HfTL), 04277, Leipzig, Germany

    Oliver Jokisch

Authors
  1. Johanna Dobbriner
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  2. Oliver Jokisch
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Correspondence to Johanna Dobbriner .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Albert Ali Salah

  2. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Dobbriner, J., Jokisch, O. (2019). Towards a Dialect Classification in German Speech Samples. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_7

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  • DOI: https://doi.org/10.1007/978-3-030-26061-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26060-6

  • Online ISBN: 978-3-030-26061-3

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