Abstract

In this paper, we study the usefulness of the best path and the complete trellis in dynamic programming based template matching approach for detecting pronunciation mismatch. We show that there exists cues in trellis (a matrix representing all paths), which could be exploited for detecting pronunciation mismatch. Such an approach could be used to build a template based approach for detecting pronunciation mismatch independent of the language

Abstract

Sandhi splitting is a primary and an important step for any natural language processing (NLP) application for languages which have agglutinative morphology. This paper presents a statistical approach to build a sandhi splitter for agglutinative languages. The input to the model is a valid string in the language and the output is a split of that string into meaningful word/s. The approach adopted comprises of two stages namely Segmentation and Word generation, both of which use conditional random fields (CRFs). Our approach is robust and language independent. The results for two Dravidian languages viz. Telugu and Malayalam show an accuracy of 89.07% and 90.50% respectively