LAUSR

In this study, performance analysis of a state-of-art phrase-based statistical machine translation (SMT) system is presented on eight Indian languages. State of the art in SMT on different Indian languages to English language has also been discussed briefly. The motivation of this study was to promote the development of SMT and linguistic resources for these Indian language pairs, as the current systems are in infancy stage due to sparse data resources. EMILLE and crowdsourcing parallel corpora have been used in this study for experimental purposes. The study is concluded by presenting the performance of baseline SMT system for Indian languages (Bengali, Gujarati, Hindi, Malayalam, Punjabi, Tamil, Telugu and Urdu) into English with average 10–20 % accurate results for all the language pairs. As a result of this study, both of these annotated parallel corpora resources and SMT system will serve as benchmarks for future approaches to SMT in Hindi → English, Urdu → English, Punjabi → English, Telugu → English, Tamil → English, Gujarati → English, Bengali → English and Malayalam → English.