Investigation of Indian English Speech Recognition using CMU Sphinx

In the recent years, research on speech recognition has given much diligence to the automatic transcription of speech data such as broadcast news (BN), medical transcription, etc. Large Vocabulary Continuous Speech Recognition (LVCSR) systems have been developed successfully for Englishes (American English (AE), British English (BE), etc.) and other languages but in case of Indian English (IE), it is still at infancy stage. IE is one of the varieties of English spoken in Indian subcontinent and is largely different from the English spoken in other parts of the world. In this paper, we have presented our work on LVCSR of IE video lectures. The speech data contains video lectures on various engineering subjects given by the experts from all over India as part of the NPTEL project which comprises of 23 hours. We have used CMU Sphinx for training and decoding in our large vocabulary continuous speech recognition experiments. The results analysis instantiate that building IE acoustic model for IE speech recognition is essential due to the fact that it has given 34% less average word error rate (WER) than HUB-4 acoustic models. The average WER before and after adaptation of IE acoustic model is 38% and 31% respectively. Even though, our IE acoustic model is trained with limited training data and the corpora used for building the language models do not mimic the spoken language, the results are promising and comparable to the results reported for AE lecture recognition in the literature.