Syllable Subword Tokens for Open Vocabulary Speech Recognition in Malayalam
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Abstract (arXiv)
In a hybrid automatic speech recognition (ASR) system, a pronunciation lexicon (PL) and a language model (LM) are essential to correctly retrieve spoken word sequences. Being a morphologically complex language, the vocabulary of Malayalam is so huge and it is impossible to build a PL and an LM that cover all diverse word forms. Usage of subword tokens to build PL and LM, and combining them to form words after decoding, enables the recovery of many out of vocabulary words. In this work we investigate the impact of using syllables as subword tokens instead of words in Malayalam ASR, and evaluate the relative improvement in lexicon size, model memory requirement and word error rate.
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References · 4
- 2021Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insightscited by 14
- 2020An investigation of phone-based subword units for end-to-end speech recognitioncited by 12
- 2021Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognitioncited by 11
- 2021A Comparison of Methods for OOV-word Recognition on a New Public Datasetcited by 2
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