Speech-Driven Natural Language Understanding Framework Chatbot: Bridging Conversational AI and Data Analytics through Integrated Voice Processing

Abstract

Abstract In many data-driven environments, people without technical backgrounds find it difficult to work with structured datasets because most existing tools require programming knowledge or familiarity with query languages. To address this problem, a voice-based system is developed that allows users to interact with data using spoken input instead of written queries. Users can ask questions verbally and receive results in both visual and audio formats. The system converts spoken input into executable Pandas commands that are applied to a preloaded dataset through a simple two-step process, where the required code is generated first and then executed. The application is implemented using a Flask backend with a lightweight frontend built using HTML and JavaScript, allowing interaction through a web browser. Initial testing indicates that the system supports hands-free data exploration and can be used effectively by individuals with little or no programming experience. Key Terms - Natural Language Understanding, Speech-to-Text, Large Language Models, Text- to-Speech, Conversational Analytics, Voice-Based Data Processing, Pandas Automation, Flask Framework, Human–Computer Interaction, Data Accessibility, and Conversational AI.

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