A Mostly Data-Driven Approach to Inverse Text Normalization

For an automatic speech recognition system to produce sensibly formatted, readable output, the spoken-form token sequence produced by the core speech recognizer must be converted to a written-form string. This process is known as inverse text normalization (ITN). Here we present a mostly data-driven ITN system that leverages a set of simple rules and a few handcrafted grammars to cast ITN as a labeling problem. To this labeling problem, we apply a compact bi-directional LSTM. We show that the approach performs well using practical amounts of training data.