Attempt Towards Stress Transfer in Speech-to-Speech Machine Translation
Sai Akarsh Vamshi Raghusimha Anindita Mondal Anil Vuppala
The language diversity in India's education sector poses a significant
challenge, hindering inclusivity. Despite the democratization of knowledge
through online educational content, the dominance of English, as the internet's
lingua franca, limits accessibility, emphasizing the crucial need for
translation into Indian languages. Despite existing Speech-to-Speech Machine
Translation (SSMT) technologies, the lack of intonation in these systems gives
monotonous translations, leading to a loss of audience interest and
disengagement from the content. To address this, our paper introduces a dataset
with stress annotations in Indian English and also a Text-to-Speech (TTS)
architecture capable of incorporating stress into synthesized speech. This
dataset is used for training a stress detection model, which is then used in
the SSMT system for detecting stress in the source speech and transferring it
into the target language speech. The TTS architecture is based on FastPitch and
can modify the variances based on stressed words given. We present an Indian
English-to-Hindi SSMT system that can transfer stress and aim to enhance the
overall quality and engagement of educational content.