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As more and more online search queries come from voice, automatic speechrecognition becomes a key component to deliver relevant search results. Errorsintroduced by automatic speech recognition (ASR) lead to irrelevant searchresults returned to the user, thus causing user dissatisfaction. In this paper,we introduce an approach, Mondegreen, to correct voice queries in text spacewithout depending on audio signals, which may not always be available due tosystem constraints or privacy or bandwidth (for example, some ASR systems runon-device) considerations. We focus on voice queries transcribed via severalproprietary commercial ASR systems. These queries come from users makinginternet, or online service search queries. We first present an analysisshowing how different the language distribution coming from user voice queriesis from that in traditional text corpora used to train off-the-shelf ASRsystems. We then demonstrate that Mondegreen can achieve significantimprovements in increased user interaction by correcting user voice queries inone of the largest search systems in Google. Finally, we see Mondegreen ascomplementing existing highly-optimized production ASR systems, which may notbe frequently retrained and thus lag behind due to vocabulary drifts.

Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries