AI Call Assistant – Extractive Summarization of Call Recordings
R. Sivaranjani1
Amalapurapu Keerthi2, Draksharapu Vaishnavi Malya3, Bhogi Bhuvanesh4, Gopi Neeraj Kumar5
[1] Bachelor of Technology Professor,
Department of Computer Science and Engineering, Raghu Engineering College, Visakhapatnam.
[2-5] Bachelor of Technology Students,
Department of Computer Science and Engineering, Raghu Institute of Technology, Visakhapatnam.
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Abstract: In today's fast-paced business landscape, effective communication is a key for success. With the increasing volume of phone calls in various industries, there is a growing need for efficient call management and analysis tools. The AI Call Assistant project aims to address this need by leveraging artificial intelligence (AI) technology to summarize call recordings, enhancing productivity and decision-making processes. The core functionality of this project revolves around its ability to process audio data from call recordings and extract key insights and information. Using advanced natural language processing (NLP) techniques, the system identifies important topics, sentiments, and actions discussed during the call. The call transcripts attained from call recording pose unique challenges that are not adequately addressed by most open-source automatic text summarizers. This project aims to contribute to the field of artificial intelligence by providing efficient and effective methods to recognize call recording and summarize, offering a valuable tool for extracting insights from spoken audio efficiently.Top of Form This research aligns with the broader fields of natural language processing (NLP), machine learning, and artificial intelligence (AI), particularly in the domain of speech recognition and understanding. It also intersects with communication technology and data analytics, focusing on optimizing call management processes and extracting actionable insights from conversational data. Moreover, it aligns with the goal of improving efficiency and decision-making in various industries through the application of AI-driven solutions. Overall, this project offers a novel approach to improving call management processes through AI-driven extractive summarization, contributing to advancements in natural language processing and communication technologies.
Key Words: Artificial Intelligence, BERT, Textual Summarization, Transformers, Natural Language Processing, Speech Recognition, Speech-To-Text.
