BUT System for Low Resource Indian Language ASR

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

Abstract:

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.

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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.

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BiBTeX Entry:

@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} }