Comparative Study of Multilingual Intelligent Virtual Assistant

Every conversational system or goal-oriented dialogue system must have spoken language understanding (SLU), which is a crucial element. To help the user achieve their desired goals, it is essential to comprehend the user's statement. The multilingual intelligent virtual assistant model based techniques for accurately reacting to the user without delay are taken into consideration by many studies in recent days, but very few review articles are available on multilingual virtual assistant. In this study, multilingual intelligent virtual assistant systems were examined. To enhance accuracy and F1 measure regarding the multilingual intelligent virtual assistant model, benefits and downsides of every strategy, challenges and potential areas for future research are observed. The challenges examined in the study such as over fitting issues, lack of datasets, failure in recognizing low resource images, and poor detection performance can be overcome by future researches.