  As more and more online search queries come from voice, automatic speech
recognition becomes a key component to deliver relevant search results. Errors
introduced by automatic speech recognition (ASR) lead to irrelevant search
results returned to the user, thus causing user dissatisfaction. In this paper,
we introduce an approach, Mondegreen, to correct voice queries in text space
without depending on audio signals, which may not always be available due to
system constraints or privacy or bandwidth (for example, some ASR systems run
on-device) considerations. We focus on voice queries transcribed via several
proprietary commercial ASR systems. These queries come from users making
internet, or online service search queries. We first present an analysis
showing how different the language distribution coming from user voice queries
is from that in traditional text corpora used to train off-the-shelf ASR
systems. We then demonstrate that Mondegreen can achieve significant
improvements in increased user interaction by correcting user voice queries in
one of the largest search systems in Google. Finally, we see Mondegreen as
complementing existing highly-optimized production ASR systems, which may not
be frequently retrained and thus lag behind due to vocabulary drifts.
