We propose an ASR system for Sanskrit, a low-resource language, that effectively combines subword tokenisation strategies and search space enrichment with linguistic information. More specifically, to address the challenges due to the high degree of out-of-vocabulary entries present in the language, we first use a subword-based language model and acoustic model to generate a search space. The search space, so obtained, is converted into a word-based search space and is further enriched with morphological and lexical information based on a shallow parser. Finally, the transitions in the search space are rescored using a supervised morphological parser proposed for Sanskrit. Our proposed approach currently reports the state-of-the-art results in Sanskrit ASR, with a 7.18 absolute point reduction in WER than the previous state-of-the-art.
Cite as: Kumar, R., Adiga, D., Ranjan, R., Krishna, A., Ramakrishnan, G., Goyal, P., Jyothi, P. (2022) Linguistically Informed Post-processing for ASR Error correction in Sanskrit. Proc. Interspeech 2022, 2293-2297, doi: 10.21437/Interspeech.2022-11189
@inproceedings{kumar22c_interspeech,
author={Rishabh Kumar and Devaraja Adiga and Rishav Ranjan and Amrith Krishna and Ganesh Ramakrishnan and Pawan Goyal and Preethi Jyothi},
title={{Linguistically Informed Post-processing for ASR Error correction in Sanskrit}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={2293--2297},
doi={10.21437/Interspeech.2022-11189}
}