Using Speech Production Knowledge for Raw Waveform Modelling Based Styrian Dialect Identification

S. Pavankumar Dubagunta, Mathew Magimai-Doss


This paper addresses the Styrian Dialect sub-challenge of the INTERSPEECH 2019 Computational Paralinguistics Challenge. We treat this challenge as dialect identification with no linguistic resources/knowledge and with limited acoustic resources, and develop end-to-end raw waveform modelling based methods that incorporate knowledge related to speech production. In this direction, we investigate two methods: (a) modelling the signals after source system decomposition and (b) transferring knowledge from articulatory feature models trained on English language. Our investigations show that the proposed approaches on the ComParE 2019 Styrian dialect data yield systems that perform better than low level descriptor-based and bag-of-audio-word representation based approaches and comparable to sequence-to-sequence auto-encoder based approach.


 DOI: 10.21437/Interspeech.2019-2398

Cite as: Dubagunta, S.P., Magimai-Doss, M. (2019) Using Speech Production Knowledge for Raw Waveform Modelling Based Styrian Dialect Identification. Proc. Interspeech 2019, 2383-2387, DOI: 10.21437/Interspeech.2019-2398.


@inproceedings{Dubagunta2019,
  author={S. Pavankumar Dubagunta and Mathew Magimai-Doss},
  title={{Using Speech Production Knowledge for Raw Waveform Modelling Based Styrian Dialect Identification}},
  year=2019,
  booktitle={Proc. Interspeech 2019},
  pages={2383--2387},
  doi={10.21437/Interspeech.2019-2398},
  url={http://dx.doi.org/10.21437/Interspeech.2019-2398}
}