The development of novel techniques that can easily extract information from the joint observations of measurements coming from many modalities is necessary to realise the full potential of futuristic information processing applications in communication industries. Communication is an essential process in the design, development, and operation of systems and is expanded upon by exploring the essential information available at every stage of the system lifetime, including identifying the needs and constraints, conveying that knowledge, and confirming and validating that the right actions have been taken. Multimodal communication describes the integration of multiple communication technologies in a single interoperation system. Multimodal and cross modal applications differ from traditional interaction methods in several ways. They can use any combination of input and output modalities, including but not limited to: video, audio, radio, fax, telephone, electronic, mechanical creating a more holistic user experience. Artificial intelligence systems, like Chabot’s and virtual assistants, may comprehend and react to users more instinctively and organically when they use multimodal artificial intelligence. It contributes to improving user experience as well as the efficacy and efficiency of interactions across a range of industries. Nevertheless, in recent times, deep learning and machine learning techniques are playing a pivotal role in multimodal signal processing for biomedical, fraud prevention, autonomous vehicles, Facial recognition, Hiring, Gaming, Social media, Travel and so on. But this domain suffers in exploiting its full potent due to absence of frameworks and tools for creating cross-modal and multimodal applications, as well as the lack of a common data structure that supports several modalities. This provides a common platform to a diverse and distinguished group of domain experts worldwide, all sharing a common passion for the ever-evolving information in
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