An Exploration towards Joint Acoustic Modeling for Indian Languages: IIIT-H Submission for Low Resource Speech Recognition Challenge for Indian Languages, INTERSPEECH 2018

India being a multilingual society, a multilingual automatic speech recognition system (ASR) is widely appreciated. Despite different orthographies, Indian languages share same phonetic space. To exploit this property, a joint acoustic model has been trained for developing multilingual ASR system us-ing a common phone-set. Three Indian languages namely Tel-ugu, Tamil and, Gujarati are considered for the study. This work studies the amenability of two different acoustic modeling approaches for training a joint acoustic model using common phone-set. Sub-space Gaussian mixture models (SGMM), and recurrent neural networks (RNN) trained with connectionst temporal classification (CTC) objective function are explored for training joint acoustic models. From the experimental re-sults, it can be observed that the joint acoustic models trained with RNN-CTC have performed better than SGMM system even on 120 hours of data (approx 40 hrs per language). The joint acoustic model trained with RNN-CTC has performed better than monolingual models, due to an efficient data sharing across the languages. Conditioning the joint model with language identity had a minimal advantage. Sub-sampling the features by a factor of 2 while training RNN-CTC models has reduced the training times and has performed better.