An Analysis on Conversational AI: The Multimodal Frontier in Chatbot System Advancements
Gunda Nikhil, Durga Revanth Yeligatla, Tejendra Chowdary Chaparala, Vishnu Chalavadi, Hardarshan Kaur, Vinay Kumar Singh · 2024
This research study analyses the evolution of conversational AI, focusing on the integration of multi-modal systems that incorporate text, audio, and visual data to create more dynamic dialogue systems. By considering the significance of visual information in enhancing Automatic Speech Recognition (ASR), this study explores early developments in the field. This study discusses about the optimization of these models by using techniques such as reinforcement learning and multi-task learning along with the use of data fusion methods to effectively integrate multimodal inputs. Key research gaps are identified, particularly the need for improved emotional intelligence and handling of diverse input modalities. This research study concludes by highlighting the potential of advanced approaches such as adversarial machine learning and transfer learning in addressing current limitations and shaping the future trajectory of conversational AI.