  Speech Translation has always been about giving source text or audio input
and waiting for system to give translated output in desired form. In this
paper, we present the Acoustic Dialect Decoder (ADD) - a voice to voice
ear-piece translation device. We introduce and survey the recent advances made
in the field of Speech Engineering, to employ in the ADD, particularly focusing
on the three major processing steps of Recognition, Translation and Synthesis.
We tackle the problem of machine understanding of natural language by designing
a recognition unit for source audio to text, a translation unit for source
language text to target language text, and a synthesis unit for target language
text to target language speech. Speech from the surroundings will be recorded
by the recognition unit present on the ear-piece and translation will start as
soon as one sentence is successfully read. This way, we hope to give translated
output as and when input is being read. The recognition unit will use Hidden
Markov Models (HMMs) Based Tool-Kit (HTK), hybrid RNN systems with gated memory
cells, and the synthesis unit, HMM based speech synthesis system HTS. This
system will initially be built as an English to Tamil translation device.
