Advancing Multilingual Communication NLP based Translational Speech to Speech Dialogue System for Indian Languages
Umansh Agarwal, Rishika Gupta, G. Saranya · 2024
Today it is more crucial than ever to remove language barriers in the linked world of today. This study compares and contrasts on-demand live translation services with an emphasis on effectiveness and accuracy. It presents a workable approach to smooth multilingual communication, with a focus on Indian languages. A state-of-the-art on-call speech translation system built on a sentence-level Neural Machine Translation (NMT) model is revealed in this work. The NMT approach translates individual sentences, as opposed to previous algorithms that work with full documents, guaranteeing accuracy and context awareness. NMT, BERT, and XL Net are also compared, emphasizing their unique qualities and areas of use. To facilitate easy communication in chosen languages, this model supports Hindi, Tamil, Telugu, Malayalam, Urdu, Bengali, and English. This helps address the linguistic variety of India. Its versatility and efficacy are a result of intensive training on a wide range of language pairings, highlighting the contribution of technology to intercultural communication. The study emphasizes how the NMT-based on-call voice translation system improves commu-nication accessibility in our multicultural, globalized society by bridging linguistic gaps, particularly in Indian languages. In addition, a unique application of multithreading has been employed to lower latency by concurrently executing the transcription and translation operations. Research shows that when multithreading is used, the time required for a translation decreases to 93.82 seconds from an initial 137 seconds. This helps to some extent with the latency issue