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We present automatic speech recognition (ASR) systems for Tamil and Kannadabased on subword modeling to effectively handle unlimited vocabulary due to thehighly agglutinative nature of the languages. We explore byte pair encoding(BPE), and proposed a variant of this algorithm named extended-BPE, andMorfessor tool to segment each word as subwords. We have effectivelyincorporated maximum likelihood (ML) and Viterbi estimation techniques withweighted finite state transducers (WFST) framework in these algorithms to learnthe subword dictionary from a large text corpus. Using the learnt subworddictionary, the words in training data transcriptions are segmented to subwordsand we train deep neural network ASR systems which recognize subword sequencefor any given test speech utterance. The output subword sequence is thenpost-processed using deterministic rules to get the final word sequence suchthat the actual number of words that can be recognized is much larger. ForTamil ASR, We use 152 hours of data for training and 65 hours for testing,whereas for Kannada ASR, we use 275 hours for training and 72 hours fortesting. Upon experimenting with different combination of segmentation andestimation techniques, we find that the word error rate (WER) reducesdrastically when compared to the baseline word-level ASR, achieving a maximumabsolute WER reduction of 6.24% and 6.63% for Tamil and Kannada respectively.

Subword Dictionary Learning and Segmentation Techniques for Automatic Speech Recognition in Tamil and Kannada