Database-Aware ASR Error Correction for Speech-to-SQL Parsing

We study the task of spoken natural language to SQL parsing (speech-to-SQL), where the goal is to map a spoken utterance to the corresponding SQL. A simple way to develop a speech-to-SQL parser is to pass the speech to an automatic speech recognition (ASR) system, and pass the transcription to a text-to-SQL parser. However, ASR is still error-prone. We propose an ASR correction method, DBATI (DataBase-Aware TaggerILM). The method first detects erroneous spans in the input, and rewrites each span. Our method leverages a novel joint representation of text and the database (DB). Our experiments show that our method yields better performance on both text quality and downstream SQL accuracy, compared to existing ASR error correction methods.