Unsupervised stemmed text corpus for language modeling and transcription of Telugu broadcast news

Abstract

In Indian Languages, root words will be either combined or modified to match the context with reference to tense, number and/or gender. So the number of unique words will increase when compared to many European languages. Whatever be the size of the text corpus used for language modeling cannot contain all the possible inflected words. A word which occurred during testing but not in training data is called Out of Vocabulary (OOV) word. Similarly, the text corpus cannot have all possible sequence of words. So Due to this data sparsity, 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. To reduce the OOVs in the language model, a new unsupervised stemming method is proposed in this paper for one Indian language, Telugu, based on the method proposed for Hindi. Other issues in the language modeling for Telugu using techniques like smoothing and interpolation, with supervised and unsupervised stemming data is also analyzed. It is observed that the smoothing techniques Witten–Bell and Kneser–Ney performing well when compared to other techniques, on pre-processed data with supervised learning. The ASRs accuracy is improved by 0.76% and 0.94% with supervised and unsupervised stemming respectively.