Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jerusha, B.; Sunil, Aleena Rose K; Yogish, Deepa Title A name given to the resource A Pipeline for Speech-to-Text Summarization and Question Identification for Enhanced Chatbot Interactions Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 3rd IEEE International Conference on Knowledge Engineering and Communication Systems, ICKECS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICKECS65700.2025.11036034" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICKECS65700.2025.11036034</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010193381?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010193381?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jerusha B., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Sunil A.R.K., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Yogish D., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India Description An account of the resource The rapid advancements in natural language processing provide strong support for the new potential application of integrating Google Speech Recognition API, BART, and BERT to create a full pipeline for speech recognition, text summarization and question answering without breaking human interaction. The research aims to develop such a holistic pipeline involves integrating the Google Speech Recognition API to perform speech-to-text, BART for text summarization, and finally BERT for question answering based on both the summary and original transcript. The system was tested under various criteria such as testing accuracy, real-time processing performance, and stress tests for scalability where the findings include an average of 60% text compression with BART, an 88% accuracy in BERT-based question answering, and scores indicating high user satisfaction (4.3/5). Real-time processing latency can be able to cater to interaction within 2-3 seconds and the capacity of the system has proven without performance loss during simultaneous users. The research done can practically find applications in areas like intelligent virtual assistants, customer service automation and e-learning applications that improve accessibility and user experience. 2025 IEEE. Subject The topic of the resource BART; BERT; Google Speech Recognition; Multi-modal NLP; Natural Language Processing; Question Answering; Speech-to-Text; Text Summarization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153701-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online