Natural Language Processing for Real-Time Transcription in VoIP Systems

Natural Language Processing (NLP) has come to play a vital role in improving the Voice over Internet Protocol (VoIP) and enabling them to offer real time transcription capabilities. In this paper, we investigate the use of promising NLP techniques such as tokenization, syntactic parsing and semantic modeling to speech transcribe the spoken language that arises during the live VoIP communications, generating accurate text transcriptions. It then studies a system of integrating Automatic Speech Recognition (ASR) systems with Deep Learning models, including Recurrent Neural Networks (RNNs) as well as the Transformer-based models, to improve the ASR transcriptions' accuracy, as well as the efficiency. Lastly, the study looks into challenges, such as background noise, speaker changes and latency, and introduces solutions, for example, noise robust NLP pipeline and end to end neural architecture. Although, the paper also talks about the use of various multilingual models which can facilitate different linguistic requirements in the context of global communication. In this regard, application of these NLP methodologies will enable VoIP systems to migrate to such state of enhanced real time transcription, resulting in improved communications and lead to increased efficiency in the information exchange in the professional and the personal environments.