Voice-based Road Navigation System Using Natural Language Processing (NLP)

Abstract

In a highly technological era, voice-based navigation systems play a major role in bridging the gap between man and machine. To overcome the difficulty in understanding the user's voice commands and natural language simulations, process the path with the user's turn by turn directions with the mention of key entities such as street names, landmarks, points of interest, connections and path mapping in an interactive interface, we propose a user-centric roadmap navigation mobile application called “Direct Me”. To generate the user's preferred path, the system will first convert audio streams to text through ASR using the Pocket Sphinx library, followed by Natural Language Processing (NLP) by taking advantage of Stanford CoreNLP Framework to retrieve navigation-related information and handle the path in the map using the Google Map API at the user's request. This system is used to provide an effective approach to translating natural language commands into a format that can be fully understood by machine and will benefit in the development of human-machine-oriented interface.