Improving Multimodal Interactions with ChatGPT by Integrating Machine Learning with Text, Image, and Speech Processing
Galiveeti Poornima, Christian Rafael Quevedo Lezama, Dankan Gowda V, S. Lalitha Kumari, Tarun Kashni, Neeraj Dadwal · 2024
The combination of multimodal interaction in artificial intelligence has become another issue that continues to attract researcher’s attention due to its capability to improve the naturalness and Natural Language Processing. This paper has offered a new approach to enhance the multimodal interactions in ChatGPT through the application of machine learning for text, image and speech. Using Convolutional Neural Network CNNs for image data, Long Short-Term Memory LSTM for temporal analysis of Voice data and Transformers for context analysis of test data. The experimental study proves the notion that the proposed system has a higher level of accuracy and less time than the existing ones. The system suffisamment encode the temporal relations and required features for correct multimodal analysis. Also, the real-time analysis and feedback-rendering capacities contribute to the enhancement of performance in a progressive and innovative manner. These results point to fruitful avenues of future work in the areas of virtual assistants, customer service, and educational technologies. The study also underlines the necessity of the integrated processing of several modalities and provides the basis for follow-up works to develop and enhance such systems in various and limited-resource conditions.