BUT System for Low Resource Indian Language ASR

Bhargav Pulugundla, Murali Karthick Baskar, Santosh Kesiraju, Ekaterina Egorova, Martin Karafiát, Lukáš Burget, Jan Černocký


This paper describes the BUT ‘Jilebi’ team’s speech recognition systems created for the 2018 low resource speech recognition challenge for Indian languages. We investigate modifications of multilingual time-delay neural network (TDNN) architectures with transfer learning and compare them to bi-directional residual memory networks (BRMN) and bi-directional LSTM. Our best submission based on system combination achieved word error rates of 13.92% (Tamil), 14.71% (Telugu) and 14.06% (Gujarati). We present the details of submitted systems and also the post-evaluation analysis done for lexicon discovery using unsupervised word segmentation.


 DOI: 10.21437/Interspeech.2018-1302

Cite as: Pulugundla, B., Baskar, M.K., Kesiraju, S., Egorova, E., Karafiát, M., Burget, L., Černocký, J. (2018) BUT System for Low Resource Indian Language ASR. Proc. Interspeech 2018, 3182-3186, DOI: 10.21437/Interspeech.2018-1302.


@inproceedings{Pulugundla2018,
  author={Bhargav Pulugundla and Murali Karthick Baskar and Santosh Kesiraju and Ekaterina Egorova and Martin Karafiát and Lukáš Burget and Jan Černocký},
  title={BUT System for Low Resource Indian Language ASR},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={3182--3186},
  doi={10.21437/Interspeech.2018-1302},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1302}
}