SD-DVQ: Semantic-Driven Data Visualization Query Generation from Speech Queries

In the era of big data, effectively leveraging vast amounts of data is one of the major challenges across industries, especially in data visualization, which helps decision-makers intuitively understand data. However, many existing data visualization tools require users to have certain technical skills, posing a barrier for users without a technical background. With the widespread use of voice interfaces, particularly smart assistants, the task of converting voice queries into data visualizations, known as ''Speech-to-Vis,'' has emerged. Yet, existing methods still face issues such as speech recognition errors and insufficient model generalization. We propose SD-DVQ, a Semantic-Driven Data Visualization Query Generation from Speech Queries framework, to address these issues. Unlike previous methods that rely on ASR-based cascades or completely end-to-end systems, SD-DVQ generates semantically consistent natural language queries from speech with schema information, and then improves downstream DVQ generation through schema filtering and database-feedback-based self-correction. Experiments on the SpeechNVBench benchmark show that SD-DVQ achieves 45.02% exact-match accuracy, outperforming the strongest baseline by 5.07 percentage points.