But System For Low Resource Indian Language Asr
19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES(2018)
Abstract
This paper describes the BUT 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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Key words
Indian languages, low resource ASR, multilingual, LF-MMI
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