  Automated Speech Recognition (ASR) is an interdisciplinary application of
computer science and linguistics that enable us to derive the transcription
from the uttered speech waveform. It finds several applications in Military
like High-performance fighter aircraft, helicopters, air-traffic controller.
Other than military speech recognition is used in healthcare, persons with
disabilities and many more. ASR has been an active research area. Several
models and algorithms for speech to text (STT) have been proposed. One of the
most recent is Mozilla Deep Speech, it is based on the Deep Speech research
paper by Baidu. Deep Speech is a state-of-art speech recognition system is
developed using end-to-end deep learning, it is trained using well-optimized
Recurrent Neural Network (RNN) training system utilizing multiple Graphical
Processing Units (GPUs). This training is mostly done using American-English
accent datasets, which results in poor generalizability to other English
accents. India is a land of vast diversity. This can even be seen in the
speech, there are several English accents which vary from state to state. In
this work, we have used transfer learning approach using most recent Deep
Speech model i.e., deepspeech-0.9.3 to develop an end-to-end speech recognition
system for Indian-English accents. This work utilizes fine-tuning and data
argumentation to further optimize and improve the Deep Speech ASR system. Indic
TTS data of Indian-English accents is used for transfer learning and
fine-tuning the pre-trained Deep Speech model. A general comparison is made
among the untrained model, our trained model and other available speech
recognition services for Indian-English Accents.
