Out-of-vocabulary word detection in a speech-to-speech translation system

In this paper we describe progress we have made in detecting out-of-vocabulary words (OOVs) for a speech-to-speech translation system for the purpose of playing back audio to the user for clarification and correction. Our OOV detector follows a strategy of first identifying a rough location of the OOV and then merging adjacent decoded words to cover the true OOV word. We show the advantage of our OOV detection strategy and report on improvements using a real-time implementation of a new Convolutional Neural Network acoustic model. We discuss why commonly used metrics for OOV detection do not meet our needs and explore an overlap metric as well as a Jaccard metric for evaluating our ability to detect the OOVs and localize them accurately in time. We have found different metrics to be useful at different stages of development.