  Virtual Assistants (VAs) are important Information Retrieval platforms that
help users accomplish various tasks through spoken commands. The speech
recognition system (speech-to-text) uses query priors, trained solely on text,
to distinguish between phonetically confusing alternatives. Hence, the
generation of synthetic queries that are similar to existing VA usage can
greatly improve upon the VA's abilities -- especially for use-cases that do not
(yet) occur in paired audio/text data.
  In this paper, we provide a preliminary exploration of the use of Large
Language Models (LLMs) to generate synthetic queries that are complementary to
template-based methods. We investigate whether the methods (a) generate queries
that are similar to randomly sampled, representative, and anonymized user
queries from a popular VA, and (b) whether the generated queries are specific.
  We find that LLMs generate more verbose queries, compared to template-based
methods, and reference aspects specific to the entity. The generated queries
are similar to VA user queries, and are specific enough to retrieve the
relevant entity. We conclude that queries generated by LLMs and templates are
complementary.
