Speech-to-Speech Translation Using Transformer Neural Network

Speech-to-speech translation is a technology that allows people to speak in one language and have their words automatically translated into another language. It can be used to facilitate communication between people who speak different languages, and it is often used in applications such as language translation applications, language translation devices, and translation software for businesses and organizations. It can be useful in a variety of situations, such as traveling to a country where you don’t speak the native language and communicating with people who speak different languages in a business or social setting, or helping people who have hearing problems to communicate with people who speak a different language. It can also be used in education and research to facilitate language learning and cross-cultural communication. Modern times have witnessed a huge leap in the field of Natural Language Processing (NLP) due to the development of deep learning models like Bidirectional Encoder Representations from Transformer (BERT), Generative Pre-trained Transformer (GPT), GPT-2, GPT-3, Robustly Optimized BERT Approach (RoBERTa), T5 (Text-to-Text Transfer Transformer), DistilBERT, etc. This paper proposes a simplistic approach for Speech-to-Speech translation using a Transformer Neural Network (TNN) which is based on an encoder–decoder architecture and makes use of an attention mechanism.