  In Indian Languages , native speakers are able to understand new words formed
by either combining or modifying root words with tense and / or gender. Due to
data insufficiency, Automatic Speech Recognition system (ASR) may not
accommodate all the words in the language model irrespective of the size of the
text corpus. It also becomes computationally challenging if the volume of the
data increases exponentially due to morphological changes to the root word. In
this paper a new unsupervised method is proposed for a Indian language: Telugu,
based on the unsupervised method for Hindi, to generate the Out of Vocabulary
(OOV) words in the language model. By using techniques like smoothing and
interpolation of pre-processed data with supervised and unsupervised stemming,
different issues in language model for Indian language: Telugu has been
addressed. We observe that the smoothing techniques Witten-Bell and Kneser-Ney
perform well when compared to other techniques on pre-processed data from
supervised learning. The ASRs accuracy is improved by 0.76% and 0.94% with
supervised and unsupervised stemming respectively.
