  Speech-based inputs have been gaining significant momentum with the
popularity of smartphones and tablets in our daily lives, since voice is the
most easiest and efficient way for human-computer interaction. This paper works
towards designing more effective speech-based interfaces to query the
structured data in relational databases. We first identify a new task named
Speech-to-SQL, which aims to understand the information conveyed by human
speech and directly translate it into structured query language (SQL)
statements. A naive solution to this problem can work in a cascaded manner,
that is, an automatic speech recognition (ASR) component followed by a
text-to-SQL component. However, it requires a high-quality ASR system and also
suffers from the error compounding problem between the two components,
resulting in limited performance. To handle these challenges, we further
propose a novel end-to-end neural architecture named SpeechSQLNet to directly
translate human speech into SQL queries without an external ASR step.
SpeechSQLNet has the advantage of making full use of the rich linguistic
information presented in speech. To the best of our knowledge, this is the
first attempt to directly synthesize SQL based on arbitrary natural language
questions, rather than a natural language-based version of SQL or its variants
with a limited SQL grammar. To validate the effectiveness of the proposed
problem and model, we further construct a dataset named SpeechQL, by
piggybacking the widely-used text-to-SQL datasets. Extensive experimental
evaluations on this dataset show that SpeechSQLNet can directly synthesize
high-quality SQL queries from human speech, outperforming various competitive
counterparts as well as the cascaded methods in terms of exact match
accuracies.
