# Wav2SQL: Direct Generalizable Speech-To-SQL Parsing

Source: [Wav2SQL: Direct Generalizable Speech-To-SQL Parsing](<https://www.sota2.com/research/paper/68fa6f641e209131cea4543d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T17:04:01.456Z

Authors: Huadai Liu, Rongjie Huang, Jinzheng He, Gang Sun, Ran Shen, Xize Cheng, Zhou Zhao

Published: 2023-05-21T19:26:46.000Z

## Abstract

Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training is faced with the major issue of data scarcity, where limited parallel data is available; and 2) the systems should be robust enough to handle diverse out-of-domain speech samples that differ from the source data. In this work, we propose the first direct speech-to-SQL parsing model Wav2SQL which avoids error compounding across cascaded systems. Specifically, 1) to accelerate speech-driven SQL parsing research in the community, we release a large-scale and multi-speaker dataset MASpider; 2) leveraging the recent progress in the large-scale pre-training, we show that it alleviates the data scarcity issue and allow for direct speech-to-SQL parsing; and 3) we include the speech re-programming and gradient reversal classifier techniques to reduce acoustic variance and learned style-agnostic representation, improving generalization to unseen out-of-domain custom data. Experimental results demonstrate that Wav2SQL avoids error compounding and achieves state-of-the-art results by up to 2.5\\% accuracy improvement over the baseline.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Speech-to-SQL Parsing on MASpider in-domain (test)](<https://www.sota2.com/research/sota/speech-to-sql-parsing-on-maspider-in-domain-test.md>): Speech-to-SQL Parsing; MASpider in-domain (test); SELECT Accuracy: 63.6.

- [Speech-to-SQL Parsing on MASpider Custom Data generalization (test)](<https://www.sota2.com/research/sota/speech-to-sql-parsing-on-maspider-custom-data-generalization-test.md>): Speech-to-SQL Parsing; MASpider Custom Data generalization (test); SELECT Component Accuracy: 61.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.12552>)

[PDF](<https://arxiv.org/pdf/2305.12552.pdf>)

[Implementation](<https://wav2sql.github.io/>)