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This paper summarizes our latest efforts in the development of a Large Vocabulary Continuous Speech Recognition (LVCSR) system for Tamil at different levels: pronunciation dictionary, language modeling (LM) and front-end. Usually in Tamil there are not many word-pronunciation pairs to train data-driven grapheme-to-phoneme (G2P) converters. Therefore, we explore the correlation between the amount of training data and the performance of the grapheme-to-phoneme (G2P) conversion. To address the morphological complexity of Tamil, we investigate different levels of morphemes for language modeling including a comparison between our Dictionary Unit Merging Algorithm (DUMA) and Morfessor, followed by various experiments on hybrid systems using word and morpheme LMs. Finally, we integrate our multilingual bottle-neck features framework with Tamil LVCSR. The final best system produced 21.34% Syllable Error Rate (SyllER) on our Tamil test set.
Premkumar et al. (Sun,) studied this question.