CONVERSION OF SPEECH TO SQL QUERY

Abstract

The conversion of speech to SQL query is an emerging interdisciplinary approach that combines speech recognition, natural language processing (NLP), and database management systems to enable intuitive human–computer interaction. This system allows users to verbally express queries in natural language, which are then automatically translated into structured SQL statements that can be executed on relational databases. The primary objective is to simplify database access for non-technical users who may lack expertise in SQL syntax. The proposed system typically consists of three main components: a speech recognition module that converts spoken input into text, an NLP engine that interprets the semantic meaning of the text, and a query generation module that maps the interpreted intent into a valid SQL query. Advanced models such as deep learning-based transformers and sequenceto-sequence architectures are often employed to improve accuracy and contextual understanding. This approach enhances accessibility, reduces dependency on technical skills, and improves efficiency in querying large datasets. However, challenges such as ambiguity in natural language, handling complex queries, and ensuring high accuracy remain significant research concerns. Overall, speech-to-SQL systems represent a promising step toward more natural and user-friendly database interaction.