  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.
