MACHINE LEARNING APPLIED TO REAL-TIME EVALUATION OF SPOKEN ENGLISH COMMUNICATION IN TOURISM

Effective interaction between travellers and local suppliers of services is critical in the increasingly international tourism business. Speaking and understanding English well is frequently essential to a satisfying trip. In the setting of tourism, this study investigates the use of machine learning algorithms for the real-time assessment of spoken English interaction. The aim of this research is to create a new system that uses algorithms based on machine learning to evaluate and enhance English-language conversations among travellers and travel agents. We provide a novel method for assessing many facets of a conversation, such as spelling, syntax, proficiency, and general sentiment, that integrates automated speech recognition (ASR), natural language processing (NLP), and sentiment analysis. The gathering of a broad collection of spoken English exchanges in travel-related contexts, the creation of a tailored ASR models taught on terminology unique to the travel industry, and the incorporation of natural language processing (NLP) methods to assess the sentiment and linguistic structure of dialogues are important aspects of the project. To assist businesses and visitors improve their ability to communicate, models based on machine learning will be taught to deliver immediate input. The goal of this project is to benefit the tourism sector by developing a tool that will enable better English-speaking interaction, which will eventually end up resulting in more satisfied and better experiences for visitors. It also covers the requirement for domain-specific individualized language instruction and evaluation tools. The study's findings could revolutionize the way spoken English proficiency is assessed and enhanced in the travel and tourism industry. They could also have wider ramifications for language acquisition and intercultural interaction across a range of sectors.