Improving homograph disambiguation with supervised machine learning

Abstract

We describe a pre-existing rule-based homograph disambiguation system used for text-to-speech synthesis at Google, and compare it against a novel system which performs disambiguation using classifiers trained on a small amount of labeled data. An evaluation of these systems, using a new, freely available English data set, finds that hybrid systems (making use of both rules and machine learning) are significantly more accurate than either hand-written rules or machine learning alone. The evaluation also finds minimal performance degradation when the hybrid system is configured to run on limited-resource mobile devices rather than on production servers. The two best systems described here are used for homograph disambiguation on all American English text-to-speech traffic at Google.

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